AI-driven inverse design of materials: Past, present and future

Bo-Wen Yao; Meng-Yuan Xu; Peng-Jie Guo; Xiao-Qi Han; Xin-De Wang; Ze-Feng Gao; Zhen Feng; Zhong-Yi Lu

REVIEW 5 major objections 6 minor 2 cited by

AI-driven inverse design of materials: Past, present and future

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that AI-driven inverse design has matured into a dominant, four-stage workflow for materials discovery, and that a survey can serve as a practical map of the field.

desk verdict A broad, current survey that is useful as a pointer to the literature, but it overclaims its comparative analysis and carries several concrete errors that a referee should flag. read the letter →

arxiv 2411.09429 v4 pith:IYH7PB77 submitted 2024-11-14 cond-mat.mtrl-sci cond-mat.supr-concs.AI

Xiao-Qi Han , Xin-De Wang , Meng-Yuan Xu , Zhen Feng , Bo-Wen Yao , Peng-Jie Guo , Ze-Feng Gao , Zhong-Yi Lu This is my paper · ORCID

classification cond-mat.mtrl-scicond-mat.supr-concs.AI

keywords inversedesignofmaterialsartificialintelligencegenerativemodelsgraphneuralnetworkslargelanguagediscoveryhigh-throughputscreeningdiffusion

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that AI has become the dominant paradigm for inverse design of materials, and that the field is mature enough to be mapped systematically. It argues that AI methods now cover every stage of the workflow, from generating candidate structures to screening, validating, and experimentally synthesizing them. A sympathetic reader would care because the survey promises to be a current resource that lets researchers place any method within the landscape. The paper explicitly frames itself as filling a gap in comparative and analytical surveys of the field.

What carries the argument

The organizing device is the four-paradigm history (experiment, theory, computation, AI) combined with the four-stage inverse design workflow. The technical machinery is the pairing of invariant graph neural networks, which predict properties while respecting translation and rotation, with equivariant graph neural networks and diffusion models, which generate structures, all backed by large datasets such as Materials Project, OQMD, and OMat24. This pairing is what lets the field claim both accurate screening and de novo generation.

What would settle it

Compare the survey's citation set against a systematically compiled list of highly cited 2023 and 2024 papers on AI-driven inverse design of materials; if a large share of that list is absent, the claim of being the latest comprehensive overview fails. The claim of filling a gap in comparative studies could also be falsified by showing that the body provides no systematic comparison of methods' advantages, disadvantages, or applicable contexts.

Watch

Extended reading notes

Core claim

The central claim is that AI-driven inverse design can be understood as a pipeline of four stages, namely design and generation, high-throughput screening, computational modeling, and experimental synthesis, with distinct AI techniques serving each stage. The survey catalogs successes across nine material families and traces the evolution from traditional machine learning through geometric graph neural networks to generative diffusion models and large language models. Its organizing thesis is that the hidden mapping between crystal structure and material property is now learnable, making structure-property prediction and structure generation tractable in ways that trial-and-error and pure theory were not. The paper also argues that the field is still incomplete: generated materials often need relaxation, conditional generation by composition is scarce, and amorphous materials lack dedicated generative algorithms.

Load-bearing premise

The survey's usefulness rests on the literature selection being representative and accurate, and the authors themselves concede that important references, models, or topics may have been omitted and expressions may be imprecise.

Editorial extensions

If this is right

Researchers can use the workflow map to locate where a new AI method fits and which gaps it fills, such as generation with space-group-number control or generative algorithms for amorphous materials.
If the map holds, a fully automated loop from generation to experimental validation is the near-term trajectory, with large language models acting as the orchestrator.
The survey implies that the field's bottleneck has shifted from model architecture to data: high-quality datasets for high-entropy alloys and other complex materials, plus benchmarks for generative models, are the limiting resource.
Established baseline models like CGCNN and ALIGNN become reference points that new work is expected to beat, making the survey's chosen citations a de facto leaderboard for the field.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The survey's promise of comparative and analytical studies is only partially delivered; the body is largely a categorized listing, so a true quantitative comparison of methods across materials classes remains an open task that the survey itself points toward.
If the four-stage workflow is correct as a description, it suggests a concrete test: measuring whether new AI tools for one stage, say generation, actually improve downstream performance at the screening stage when plugged into the same workflow.
The rapid pace of the field implies the survey's value as a latest overview will decay; a version that includes citation-weighted or performance-ranked tables would stay useful longer.
The emphasis on large language models suggests a testable extension: use a domain-adapted LLM to extract candidate materials from the literature and check whether the recovered materials match the ones human experts compiled in the survey's sections.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is a broad survey, not a research contribution. Its real value is as a curated pointer to recent work across nine material classes and several AI method families. The organizing taxonomy—four paradigms, invariant vs equivariant GNNs—is standard and not new, but the aggregation is useful for someone entering the field.

The paper does several things well. The material-class chapters are dense with citations, the dataset table is handy, and the future-directions section touches on real open problems. The authors are also honest: in 'Seeking for Advise' they explicitly concede potential omissions and imprecise expressions. That is more candor than many surveys show.

The soft spots are real but not catastrophic. The introduction promises 'comparative and analytical studies' that the body does not actually deliver; it is a categorized listing without a head-to-head comparison protocol. The stress-test note is right about the internal errors: Eq. (4) is garbled, the Matformer paragraph cites [73] (CGCNN) instead of the Matformer paper, DimeNet is placed in the equivariant group despite being invariant, and FlowMM and CrystalGAN appear under diffusion models. Those are the kind of mistakes that make a reader hesitate to treat the survey as a precise map. The self-citations to InvDesFlow and MatALtMag are a selection-bias issue, not a circularity problem, and not unusual in a survey.

On balance, the conditional verdict holds. If the authors correct the errors and soften the 'comparative/analytical' claim, this becomes a useful resource. As it stands, I would use it as a starting point for literature hunting, not as a definitive reference.

Who should read it? A graduate student or new entrant wanting a quick map of recent AI-driven materials design up to late 2024. A specialist will not learn much. It deserves a serious referee: the breadth is there, and the issues are fixable. I would ask for a revision before acceptance, but not a desk reject.

Referee Report

5 major / 6 minor

Summary. This manuscript is a survey of AI-driven inverse design of materials. It is organized in two main parts: Section 2 surveys AI applications for specific functional material classes (superconductors, magnetic materials, thermoelectrics, carbon nanomaterials, 2D materials, photovoltaics, catalysts, high-entropy alloys, and porous materials), while Section 3 surveys AI methods, including traditional machine learning, geometric graph neural networks, discriminative AI, generative AI, large language models, and datasets. The authors claim that the survey provides the latest comprehensive overview of the field and fills a gap left by previous surveys by offering a comparative and analytical examination from the perspectives of functional-materials discovery and AI-method development.

Significance. If the survey delivered on its stated promise, it would be a useful entry point for researchers entering this broad and rapidly moving field. The manuscript has genuine strengths: the four-paradigm framing in Section 1 gives a readable historical narrative; Figure 4 offers a compact timeline of AI methods; Table 2 collects commonly used datasets in one place; and the authors explicitly maintain an update log, which is a helpful service to the community. However, the paper's central claim of providing a systematic comparative and analytical study is not met by the body of the text, and several technical descriptions contain errors. As a curated pointer to the literature the survey has value, but as a reliable map of the field it needs substantial revision.

major comments (5)

[Section 1 and Sections 2-3] The introduction states that previous surveys lack 'comparative and analytical studies' and that this survey will fill that gap, but the body does not deliver such a comparison. Sections 2.1-2.9 and 3.1-3.5 consist of sequential summaries of individual papers, with no stated literature-selection criteria, no common evaluation protocol, no head-to-head performance tables, and no explicit discussion of trade-offs among methods. For example, Section 2 compares methods within material classes only indirectly, and Section 3 introduces models without ever systematically comparing their accuracy, data requirements, or applicability. This is a load-bearing issue because the claimed contribution is precisely the analytical comparison, not the individual summaries.
[Section 3.2 and Section 3.4] The taxonomy of geometric GNNs is internally inconsistent. Section 3.2 places DimeNet in the equivariant GNN group, despite DimeNet being an invariant model that uses only pairwise distances and angles; the survey itself had earlier described DimeNet as an invariant distance- and angle-based model. Similarly, Section 3.4 groups FlowMM and CrystalGAN under 'diffusion generative models' together with DDPM and score-based methods, although FlowMM is a flow-matching model and CrystalGAN is a GAN. These misclassifications matter because the survey claims to provide a systematic map of AI methods and their development routes.
[Section 3.1, Eq. (4)] Equation (4) is garbled and unusable as a definition of convolution. The text reads 'f k ij denotes the output feature map for filter k at spatial position Wk mn represents the weights in the convolutional kernel k of size X(i+m)(j+n) is the input feature map,' which lacks the missing index specification, summation range, and proper variable definitions. For a survey that promises to 'systematically analyze the latest advancements,' such an imprecise technical description undermines the reader's ability to rely on the text.
[Section 3.3, Matformer paragraph] The Matformer description cites reference [73] (CGCNN) instead of the Matformer paper, and it attributes statements to 'Some researchers' in a way that obscures the source. Specifically, the sentence 'Some researchers use both multi-edge graph construction and fully-connected graph construction ... to build their Matformer [73]' should cite the original Matformer work. This is a concrete reference error that matters in a survey whose value depends on reliable pointers to the literature.
[Section 5 and 'Seeking for Advise'] The 'Seeking for Advise' section concedes that the survey 'may still have many shortcomings, such as the potential omission of important references, models, methods, or topics, as well as the possibility of imprecise expressions and discussions.' Together with the absence of any stated inclusion criteria for the papers and models covered in Sections 2 and 3, this concession makes it difficult to verify the abstract's claim that the survey is a comprehensive and authoritative resource. The authors should either add explicit selection criteria and a comparison framework, or substantially soften the comprehensiveness claim.

minor comments (6)

[Section 1] The phrase 'this research filed' should be 'this research field.'
[Section 2.6] The word 'poined' in the photovoltaic subsection should be 'pointed.'
[Section 2.9] The term 'variation autoencoder (V AE)' should be 'variational autoencoder (VAE).'
[Section 3.3] There are multiple typos in the MMPT paragraph, including 'Metux masking' (should be 'Mutex masking') and 'stoopgrad' (should be 'stopgrad').
[Section 3.5 and references] The text refers to 'MatSciBERT' and cites reference [371] in one place and reference [376] in another; the reference numbering should be checked for consistency.
[Section 4.1] The phrase 'the forth one is that' should be 'the fourth one is that.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; this is a literature survey with no fitted-input predictions or self-referential derivation chain.

full rationale

The paper is a survey of AI-driven inverse design of materials and contains no derivation chain, no fitted parameters renamed as predictions, and no uniqueness theorem invoked to force a choice. Its central claim is that it provides a current, useful overview of the literature, which is a completeness and accuracy claim rather than a result derived from its own inputs. The only self-referential elements are descriptions of the authors' own prior works, InvDesFlow [72] and MatALtMag [74], presented as examples of AI-accelerated discovery with DFT-validated outcomes; these are not used as load-bearing premises that make the survey's claims true by construction, and they are independently checkable against the cited calculations and databases. The appended 'Seeking for Advise' section concedes possible omissions and imprecise expressions, which bears on quality and completeness, not on circularity. Accordingly, no circular step can be exhibited, and the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no new fitted parameters or hypothetical entities. Its value depends on the accuracy of its literature summaries and on the editorial taxonomy, which are captured in the axioms above.

assumptions (3)

domain assumption The descriptions of cited works in this survey are accurate enough to serve as a reliable resource.
The central promise of the paper is to be a 'useful resource'; if many descriptions are wrong, the resource fails. The paper itself admits possible imprecise expressions in the 'Seeking for Advise' section.
ad hoc to paper The four-paradigm taxonomy (experiment, theory, computation, AI) is a meaningful organizing scheme for the field.
This categorization is introduced by the authors in Section 1 to structure the survey; it is not established or quantitatively validated in the referenced literature.
domain assumption AI/ML models can effectively characterize structure-property relationships in materials, making the surveyed applications legitimate.
This is the background premise of the entire field being reviewed; the survey does not prove it but relies on it to frame the applications as successes.

how reviews work

0 comments

Cite this review

Pith. "Pith review of AI-driven inverse design of materials: Past, present and future." pith.science (2026). https://pith.science/paper/IYH7PB77

@misc{pith2026241109429,
  author       = {Pith},
  title        = {Pith review of: AI-driven inverse design of materials: Past, present and future},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IYH7PB77}},
  note         = {Machine review of arXiv:2411.09429}
}

read the original abstract

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers.

Figures

Figures reproduced from arXiv: 2411.09429 by the authors.

**Figure 1.** Trends in publications and citations in the field of “Machine Learning Materials Discovery” from 2019 to 2024. The left panel [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Materials Science Research Paradigms This figure illustrates the evolution of research paradigms in materials science, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** AI-driven discovery of materials. This figure illustrates the role of artificial intelligence in accelerating the discovery of [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The rapid advancement of AI technologies accelerates materials discovery in various ways. Blue indicates traditional machine [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

**Figure 5.** Figure 5: The general process of materials inverse design, which is divided into two main parts: the AI-driven theoretical calculation [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]

**Figure 6.** Figure 6: The future of AI-driven inverse design of materials. This figure outlines the potential development directions of AI in key [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Accelerated Discovery of Vanadium Oxide Compositions: A WGAN-VAE Framework for Materials Design
cond-mat.mtrl-sci 2025-01 reject novelty 6.0 of 10

A WGAN-VAE pipeline generates vanadium oxide candidates, reporting 20% stable structures and two V2O3 phases below the Materials Project convex hull, though reference-frame and convergence issues weaken the claim.
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
cond-mat.mtrl-sci 2024-12 conditional novelty 6.0 of 10

CHIPS-FF benchmarks 16 universal machine learning force fields on 104 semiconductor materials across elastic, phonon, surface, defect, interface, and amorphous properties, finding that no model works well for interfaces.

Reference graph

Works this paper leans on

299 extracted references · 31 canonical work pages · cited by 2 Pith papers

[73]

Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,

T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Phys. Rev. Lett. , vol. 120, p. 145301, Apr 2018. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.120.145301

doi:10.1103/physrevlett.120.145301 2018
[1]

Inverse design in search of materials with target functionalities,

A. Zunger, “Inverse design in search of materials with target functionalities,” Nature Reviews Chemistry, vol. 2, no. 4, p. 0121, 2018

2018
[2]

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,

J. Lee, D. Park, M. Lee, H. Lee, K. Park, I. Lee, and S. Ryu, “Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,” Materials Horizons, 2023

2023
[3]

Inverse design of materials by machine learning,

J. Wang, Y . Wang, and Y . Chen, “Inverse design of materials by machine learning,” Materials, vol. 15, no. 5, p. 1811, 2022

2022
[4]

Generative deep learning for the inverse design of materials

T. Long, Y . Zhang, and H. Zhang, “Generative deep learn- ing for the inverse design of materials,” arXiv preprint arXiv:2409.19124, 2024

work page Pith review arXiv 2024
[5]

Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,

Y . Wu, “Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,” 2024

2024
[6]

Generative models for inverse design of inorganic solid materials,

L. Chen, W. Zhang, Z. Nie, S. Li, and F. Pan, “Generative models for inverse design of inorganic solid materials,” J. Mater. Inform, vol. 1, no. 4, 2021

2021
[7]

Inverse design of 3d cellular materials with physics-guided machine learning,

M. Abu-Mualla and J. Huang, “Inverse design of 3d cellular materials with physics-guided machine learning,”Materials & Design, vol. 232, p. 112103, 2023

2023

Show all 299 references

[8]

Further experiments with liquid helium,

H. K. Onnes, “Further experiments with liquid helium,” in Proceedings of the KNAW , vol. 13. sn, 1911, pp. 1910– 1911

1911
[9]

Superconductivity at 39 k in magnesium diboride,

J. Nagamatsu, N. Nakagawa, T. Muranaka, Y . Zenitani, and J. Akimitsu, “Superconductivity at 39 k in magnesium diboride,” nature, vol. 410, no. 6824, pp. 63–64, 2001

2001
[10]

Inverse design of porous ma- terials using artificial neural networks,

B. Kim, S. Lee, and J. Kim, “Inverse design of porous ma- terials using artificial neural networks,” Science advances, vol. 6, no. 1, p. eaax9324, 2020

2020
[11]

The dirac equation and the prediction of antimatter,

D. Vidmar, “The dirac equation and the prediction of antimatter,” PDF document provided on the internet by the Universidade Federal do Rio Grande do Sul, 2011. 30

2011
[12]

The apparent existence of easily de- flectable positives,

C. D. Anderson, “The apparent existence of easily de- flectable positives,”Science, vol. 76, no. 1967, pp. 238–239, 1932

1967
[13]

Theory of superconductivity,

J. Bardeen, L. N. Cooper, and J. R. Schrieffer, “Theory of superconductivity,” Physical review, vol. 108, no. 5, p. 1175, 1957

1957
[14]

Theory of the meissner effect in superconduc- tors,

J. Bardeen, “Theory of the meissner effect in superconduc- tors,” Physical Review, vol. 97, no. 6, p. 1724, 1955

1955
[15]

Bound electron pairs in a degenerate fermi gas,

L. N. Cooper, “Bound electron pairs in a degenerate fermi gas,” Physical Review, vol. 104, no. 4, p. 1189, 1956

1956
[16]

Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,

G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B , vol. 54, pp. 11 169–11 186, Oct 1996. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.54.11169

1996 doi
[17]

Generalized gradient approximation made simple,

J. P. Perdew, K. Burke, and M. Ernzerhof, “Generalized gradient approximation made simple,” Phys. Rev. Lett. , vol. 77, pp. 3865–3868, Oct 1996. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.77.3865

1996 doi
[18]

Projector augmented-wave method,

P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B , vol. 50, pp. 17 953–17 979, Dec
[19]

Electric field effect in atomically thin carbon films,

K. S. Novoselov, A. K. Geim, S. V . Morozov, D.-e. Jiang, Y . Zhang, S. V . Dubonos, I. V . Grigorieva, and A. A. Firsov, “Electric field effect in atomically thin carbon films,” sci- ence, vol. 306, no. 5696, pp. 666–669, 2004

2004
[20]

Theoretical models incorporating electron correlation,

J. A. Pople, J. S. Binkley, and R. Seeger, “Theoretical models incorporating electron correlation,” International Journal of Quantum Chemistry, vol. 10, no. S10, pp. 1–19, 1976

1976
[21]

Has generative artificial in- telligence solved inverse materials design?

H. Park, Z. Li, and A. Walsh, “Has generative artificial in- telligence solved inverse materials design?” Matter, vol. 7, no. 7, pp. 2355–2367, 2024

2024
[22]

Attention is all you need,

A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017

2017
[23]

Crystal diffusion variational autoencoder for periodic material generation,

T. Xie, X. Fu, O.-E. Ganea, R. Barzilay, and T. Jaakkola, “Crystal diffusion variational autoencoder for periodic material generation,” 2022. [Online]. Available: https://arxiv.org/abs/2110.06197

2022 arXiv
[24]

Crystal structure prediction by joint equivariant diffusion,

R. Jiao, W. Huang, P. Lin, J. Han, P. Chen, Y . Lu, and Y . Liu, “Crystal structure prediction by joint equivariant diffusion,” NeurIPS, vol. 36, pp. 17 464–17 497, 2023. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/ 2023/file/38b787fc530d0b31825827e2c...

2023
[25]

Mattergpt: A generative trans- former for multi-property inverse design of solid-state ma- terials,

Y . Chen, X. Wang, X. Deng, Y . Liu, X. Chen, Y . Zhang, L. Wang, and H. Xiao, “Mattergpt: A generative trans- former for multi-property inverse design of solid-state ma- terials,” arXiv preprint arXiv:2408.07608, 2024

2024 arXiv
[26]

Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design,

K. Choudhary, “Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design,” The Journal of Physical Chemistry Letters , vol. 15, no. 27, pp. 6909–6917, 2024

2024
[27]

Denoising diffusion probabilistic models for generative alloy design,

P. Fernandez-Zelaia, S. Thapliyal, R. Kannan, P. Nandwana, Y . Yamamoto, A. Nycz, V . Paquit, and M. M. Kirka, “Denoising diffusion probabilistic models for generative alloy design,” Additive Manufacturing, p. 104478, 2024

2024
[28]

Microstructure reconstruction of 2d/3d random materials via diffusion-based deep generative models,

X. Lyu and X. Ren, “Microstructure reconstruction of 2d/3d random materials via diffusion-based deep generative models,” Scientific Reports, vol. 14, no. 1, p. 5041, 2024

2024
[30]

dziner: Rational inverse design of materials with ai agents,

M. Ansari, J. Watchorn, C. E. Brown, and J. S. Brown, “dziner: Rational inverse design of materials with ai agents,” arXiv preprint arXiv:2410.03963, 2024

2024 arXiv
[31]

Scaling deep learning for materials discovery,

A. Merchant, S. Batzner, S. S. Schoenholz, M. Aykol, G. Cheon, and E. D. Cubuk, “Scaling deep learning for materials discovery,”Nature, vol. 624, no. 7990, pp. 80–85, 2023

2023
[32]

Open materials 2024 (omat24) inorganic materi- als dataset and models,

L. Barroso-Luque, M. Shuaibi, X. Fu, B. M. Wood, M. Dzamba, M. Gao, A. Rizvi, C. L. Zitnick, and Z. W. Ulissi, “Open materials 2024 (omat24) inorganic materi- als dataset and models,” arXiv preprint arXiv:2410.12771 , 2024

2024 arXiv
[33]

Inverse design of photonic and phononic topological insulators: a review,

Y . Chen, Z. Lan, Z. Su, and J. Zhu, “Inverse design of photonic and phononic topological insulators: a review,” Nanophotonics, vol. 11, no. 19, pp. 4347–4362, 2022

2022
[34]

Machine learning design for high- entropy alloys: models and algorithms,

S. Liu and C. Yang, “Machine learning design for high- entropy alloys: models and algorithms,” Metals, vol. 14, no. 2, p. 235, 2024

2024
[35]

A survey of geometric graph neural networks: Data structures, models and applications,

J. Han, J. Cen, L. Wu, Z. Li, X. Kong, R. Jiao, Z. Yu, T. Xu, F. Wu, Z. Wang, H. Xu, Z. Wei, Y . Liu, Y . Rong, and W. Huang, “A survey of geometric graph neural networks: Data structures, models and applications,” 2024. [Online]. Available: https://arxiv.org/abs/2403.00485

2024 arXiv
[36]

Graph neural networks for materials science and chemistry,

P. Reiser, M. Neubert, A. Eberhard, L. Torresi, C. Zhou, C. Shao, H. Metni, C. van Hoesel, H. Schopmans, T. Sommer, and P. Friederich, “Graph neural networks for materials science and chemistry,” Communications Materials, vol. 3, no. 1, p. 93, 2022. [Online]. Available: https:...

2022 doi
[37]

Novel technologies and configurations of superconducting magnets for mri,

Y . Lvovsky, E. W. Stautner, and T. Zhang, “Novel technologies and configurations of superconducting magnets for mri,” Supercond. Sci. Technol. , vol. 26, p. 093001, 2013. [Online]. Available: https://api.semanticscholar.org/CorpusID:121285609

2013
[38]

High temperature su- perconductors for fusion magnets,

P. Bruzzone, W. H. Fietz, J. V . Minervini, M. Novikov, N. Yanagi, Y . Zhai, and J. Zheng, “High temperature su- perconductors for fusion magnets,” Nucl. Fusion , vol. 58, p. 103001, 2018

2018
[39]

Superconducting qubit to optical photon transduction,

M. Mirhosseini, A. Sipahigil, M. Kalaee, and O. Painter, “Superconducting qubit to optical photon transduction,” Nature, vol. 588, p. 599, 2020

2020
[40]

Building logical qubits in a superconducting quantum computing system,

J. M. Gambetta, J. M. Chow, and M. Steffen, “Building logical qubits in a superconducting quantum computing system,” NPJ Quantum Inf., vol. 3, p. 2, 2017

2017
[41]

Quantum sensing,

C. L. Degen, F. Reinhard, and P. Cappellaro, “Quantum sensing,” Rev. Mod. Phys. , vol. 89, p. 035002, Jul
[42]

The resistance of pure mercury at helium temperatures,

H. K. Onnes, “The resistance of pure mercury at helium temperatures,” Commun. Phys. Lab. Univ. Leiden, b , vol. 120, 1911. [Online]. Available: https: //cir.nii.ac.jp/crid/1572543024905854592

1911
[43]

Ein neuer effekt bei eintritt der supraleitfähigkeit,

W. Meissner and R. Ochsenfeld, “Ein neuer effekt bei eintritt der supraleitfähigkeit,” Naturwissenschaften, vol. 21, no. 44, pp. 787–788, 1933. [Online]. Available: https://doi.org/10.1007/BF01504252

1933 doi
[44]

Superconductivity in nb–ge films above 22 k,

J. Gavaler, “Superconductivity in nb–ge films above 22 k,” Applied Physics Letters, vol. 23, no. 8, pp. 480–482, 1973. 31

1973
[45]

Possible high t c super- conductivity in the ba- la- cu- o system,

J. G. Bednorz and K. A. Müller, “Possible high t c super- conductivity in the ba- la- cu- o system,” Zeitschrift für Physik B Condensed Matter , vol. 64, no. 2, pp. 189–193, 1986

1986
[46]

Superconductivity at 93 k in a new mixed-phase y-ba-cu-o compound system at ambient pressure,

M. K. Wu, J. R. Ashburn, C. J. Torng, P. H. Hor, R. L. Meng, L. Gao, Z. J. Huang, Y . Q. Wang, and C. W. Chu, “Superconductivity at 93 k in a new mixed-phase y-ba-cu-o compound system at ambient pressure,” Phys. Rev. Lett. , vol. 58, pp. 908–910, Mar 1987. [Online]. Available:...

1987 doi
[47]

Superconductivity at 55 k in iron-based f-doped layered quaternary compound sm[o 1−xfx] feas,

R. Zhi-An, L. Wei, Y . Jie, Y . Wei, S. Xiao-Li, C. Guang- Can, D. Xiao-Li, S. Li-Ling, Z. Fang, and Z. Zhong-Xian, “Superconductivity at 55 k in iron-based f-doped layered quaternary compound sm[o 1−xfx] feas,” Chinese Physics Letters, vol. 25, no. 6, p. 2215, 2008

2008
[48]

Record high 36 k transition temperature to the superconducting state of elemental scandium at a pressure of 260 gpa,

J. Ying, S. Liu, Q. Lu, X. Wen, Z. Gui, Y . Zhang, X. Wang, J. Sun, and X. Chen, “Record high 36 k transition temperature to the superconducting state of elemental scandium at a pressure of 260 gpa,” Phys. Rev. Lett., vol. 130, p. 256002, Jun 2023. [Online]. Available: https:/...

2023 doi
[49]

Signatures of superconductivity near 80 K in a nickelate under high pressure,

H. Sun, M. Huo, X. Hu, J. Li, Z. Liu, Y . Han, L. Tang, Z. Mao, P. Yang, B. Wang, J. Cheng, D.-X. Yao, G.-M. Zhang, and M. Wang, “Signatures of superconductivity near 80 K in a nickelate under high pressure,” Nature, vol. 621, no. 7979, pp. 493–498, 2023

2023
[50]

Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system,

A. Drozdov, M. Eremets, I. Troyan, V . Ksenofontov, and S. I. Shylin, “Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system,” Nature, vol. 525, no. 7567, pp. 73–76, 2015

2015
[51]

Machine learning modeling of superconducting critical temperature,

V . Stanev, C. Oses, A. G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo, and I. Takeuchi, “Machine learning modeling of superconducting critical temperature,” npj Computational Materials, vol. 4, p. 29, Jun 2018. [Online]. Available: https://doi.org/10.1038/s41524-018-0085-8

2018 doi
[52]

A general-purpose machine learning framework for predict- ing properties of inorganic materials,

L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, “A general-purpose machine learning framework for predict- ing properties of inorganic materials,” npj Computational Materials, vol. 2, no. 1, pp. 1–7, 2016

2016
[53]

A deep learning approach to search for superconductors from electronic bands,

J. Li, W. Fang, S. Jin, T. Zhang, Y . Wu, X. Xu, Y . Liu, and D.-X. Yao, “A deep learning approach to search for superconductors from electronic bands,” 2024. [Online]. Available: https://arxiv.org/abs/2409.07721

2024 arXiv
[54]

Superband: an electronic-band and fermi surface structure database of superconductors,

T. Zhang, C. Suo, Y . Wu, X. Xu, Y . Liu, D.-X. Yao, and J. Li, “Superband: an electronic-band and fermi surface structure database of superconductors,” 2024. [Online]. Available: https://arxiv.org/abs/2409.09419

2024 arXiv
[55]

The graph neural network model,

F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks , vol. 20, no. 1, pp. 61–80, 2008

2008
[56]

A comprehensive survey on graph neural networks,

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y . Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020

2020
[57]

Semi-supervised classifica- tion with graph convolutional networks,

T. N. Kipf and M. Welling, “Semi-supervised classifica- tion with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016

2016 arXiv
[58]

Inductive rep- resentation learning on large graphs,

W. Hamilton, Z. Ying, and J. Leskovec, “Inductive rep- resentation learning on large graphs,” Advances in neural information processing systems, vol. 30, 2017

2017
[59]

Graph attention networks,

P. Veli ˇckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y . Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017

2017 arXiv
[60]

Convo- lutional neural networks on graphs with fast localized spec- tral filtering,

M. Defferrard, X. Bresson, and P. Vandergheynst, “Convo- lutional neural networks on graphs with fast localized spec- tral filtering,” Advances in neural information processing systems, vol. 29, 2016

2016
[61]

Designing high-tc super- conductors with bcs-inspired screening, density functional theory, and deep-learning,

K. Choudhary and K. Garrity, “Designing high-tc super- conductors with bcs-inspired screening, density functional theory, and deep-learning,” NPJ Comput. Mater. , vol. 8, no. 1, p. 244, 2022

2022
[62]

Atomistic line graph neural network for improved materials property predictions,

K. Choudhary and B. DeCost, “Atomistic line graph neural network for improved materials property predictions,” NPJ Comput. Mater., vol. 7, no. 1, p. 185, 2021

2021
[63]

Data-driven design of high pressure hydride superconductors using dft and deep learn- ing,

D. Wines and K. Choudhary, “Data-driven design of high pressure hydride superconductors using dft and deep learn- ing,” Materials futures, vol. 3, no. 2, p. 025602, 2024

2024
[64]

Machine learning model for predicting the critical transition temperature of hydride superconductors,

J. ZHAO, J. W ANG, D. HE, J. LI, Y . SUN, X.-Q. CHEN, and P. LIU, “Machine learning model for predicting the critical transition temperature of hydride superconductors,” Acta Metall Sin, vol. 60, no. 10, pp. 1418–1428, 2024

2024
[65]

Machine learning accelerated discovery of superconduct- ing two-dimensional janus transition metal sulfhydrates,

J. Li, L. Wei, X. Shi, L. Shi, J. Si, P.-F. Liu, and B.-T. Wang, “Machine learning accelerated discovery of superconduct- ing two-dimensional janus transition metal sulfhydrates,” Physical Review B, vol. 109, no. 17, p. 174516, 2024

2024
[66]

Searching materials space for hydride superconductors at ambient pressure,

T. F. Cerqueira, Y .-W. Fang, I. Errea, A. Sanna, and M. A. Marques, “Searching materials space for hydride superconductors at ambient pressure,”Advanced Functional Materials, p. 2404043, 2024

2024
[67]

3dsc- a dataset of superconductors including crystal structures,

T. Sommer, R. Willa, J. Schmalian, and P. Friederich, “3dsc- a dataset of superconductors including crystal structures,” Scientific Data, vol. 10, no. 1, p. 816, 2023

2023
[68]

High-resolution image synthesis with latent diffusion models,

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Om- mer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 684– 10 695

2022
[69]

Denoising diffusion proba- bilistic models,

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion proba- bilistic models,” Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020

2020
[70]

Generative modeling by estimating gradients of the data distribution,

Y . Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in neural information processing systems, vol. 32, 2019

2019
[71]

Accurate structure prediction of biomolecular interactions with alphafold 3,

J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Bal- lard, J. Bambrick et al. , “Accurate structure prediction of biomolecular interactions with alphafold 3,” Nature, pp. 1– 3, 2024

2024
[72]

Ai-accelerated discovery of high critical temperature superconductors,

X.-Q. Han, Z. Ouyang, P.-J. Guo, H. Sun, Z.-F. Gao, and Z.-Y . Lu, “Ai-accelerated discovery of high critical temperature superconductors,” 2024. [Online]. Available: https://arxiv.org/abs/2409.08065

2024 arXiv
[74]

Ai-accelerated discovery of altermagnetic materials,

Z.-F. Gao, S. Qu, B. Zeng, Y . Liu, J.-R. Wen, H. Sun, P.-J. Guo, and Z.-Y . Lu, “Ai-accelerated discovery of altermagnetic materials,” arXiv, vol. 2311.04418, 2024. [Online]. Available: https://arxiv.org/abs/2311.04418 32

2024 arXiv
[75]

Graph networks as a universal machine learning framework for molecules and crystals,

C. Chen, W. Ye, Y . Zuo, C. Zheng, and S. P. Ong, “Graph networks as a universal machine learning framework for molecules and crystals,” Chem. Mater , vol. 31, no. 9, pp. 3564–3572, 2019

2019
[76]

Machine learning guided discovery of superconducting calcium borocarbides,

C. Zhang, H. Tang, C. Pan, H. Jiang, H.-J. Sun, K.-M. Ho, and C.-Z. Wang, “Machine learning guided discovery of superconducting calcium borocarbides,” Physical Review B, vol. 108, no. 2, Jul. 2023. [Online]. Available: http://dx.doi.org/10.1103/PhysRevB.108.024512

2023 doi
[77]

Dpa-2: Towards a universal large atomic model for molecular and material simulation,

D. Zhang, X. Liu, X. Zhang, C. Zhang, C. Cai, H. Bi, Y . Du, X. Qin, J. Huang, B. Li et al., “Dpa-2: Towards a universal large atomic model for molecular and material simulation,” arXiv, vol. 2312.15492, 2023. [Online]. Available: https://arxiv.org/abs/2312.15492

2023 arXiv
[78]

Inverse design of next-generation superconductors using data-driven deep generative models,

D. Wines, T. Xie, and K. Choudhary, “Inverse design of next-generation superconductors using data-driven deep generative models,” The Journal of Physical Chemistry Letters, vol. 14, no. 29, pp. 6630–6638, 2023

2023
[79]

Virtual node graph neural network for full phonon prediction,

R. Okabe, A. Chotrattanapituk, A. Boonkird, N. Andrejevic, X. Fu, T. S. Jaakkola, Q. Song, T. Nguyen, N. Drucker, S. Mu, Y . Wang, B. Liao, Y . Cheng, and M. Li, “Virtual node graph neural network for full phonon prediction,” Nature Computational Science , vol. 4, no. 7, pp. 5...

2024 doi
[80]

Accelerating the calculation of electron–phonon coupling strength with machine learning,

Y . Zhong, S. Liu, B. Zhang, Z. Tao, Y . Sun, W. Chu, X.-G. Gong, J.-H. Yang, and H. Xiang, “Accelerating the calculation of electron–phonon coupling strength with machine learning,” Nature Computational Science , vol. 4, no. 8, pp. 615–625, aug 2024. [Online]. Available: http...

2024 doi
[81]

Scaling of transition temperature and cuo2 plane buckling in a high-temperature superconductor,

O. Chmaissem, J. Jorgensen, S. Short, A. Knizhnik, Y . Eck- stein, and H. Shaked, “Scaling of transition temperature and cuo2 plane buckling in a high-temperature superconductor,” Nature, vol. 397, no. 6714, pp. 45–48, 1999

1999
[82]

Relationship between crystal structure and superconduc- tivity in iron-based superconductors,

C. Lee, K. Kihou, A. Iyo, H. Kito, P. Shirage, and H. Eisaki, “Relationship between crystal structure and superconduc- tivity in iron-based superconductors,” Solid State Commu- nications, vol. 152, no. 8, pp. 644–648, 2012

2012
[83]

Anion height dependence of tc for the fe-based supercon- ductor,

Y . Mizuguchi, Y . Hara, K. Deguchi, S. Tsuda, T. Yam- aguchi, K. Takeda, H. Kotegawa, H. Tou, and Y . Takano, “Anion height dependence of tc for the fe-based supercon- ductor,” Superconductor Science and Technology , vol. 23, no. 5, p. 054013, 2010

2010
[84]

Influence of apical oxygen on the extent of in-plane exchange interaction in cuprate superconductors,

Y . Peng, G. Dellea, M. Minola, M. Conni, A. Amorese, D. Di Castro, G. De Luca, K. Kummer, M. Salluzzo, X. Sun et al., “Influence of apical oxygen on the extent of in-plane exchange interaction in cuprate superconductors,” Nature Physics, vol. 13, no. 12, pp. 1201–1206, 2017

2017
[85]

Bond sensitive graph neural networks for predicting high temperature superconductors,

L. Gu, Y . Liu, P. Chen, H. Huang, N. Chen, Y . Li, T. Lookman, Y . Lu, and Y . Su, “Bond sensitive graph neural networks for predicting high temperature superconductors,” Materials Genome Engineering Advances, p. e48, 2024

2024
[86]

Closed-loop superconducting materials discovery,

E. A. Pogue, A. New, K. McElroy, N. Q. Le, M. J. Pekala, I. McCue, E. Gienger, J. Domenico, E. Hedrick, T. M. McQueen, B. Wilfong, C. D. Piatko, C. R. Ratto, A. Lennon, C. Chung, T. Montalbano, G. Bassen, and C. D. Stiles, “Closed-loop superconducting materials discovery,” npj...
[87]

B. D. Cullity and C. D. Graham, Introduction to magnetic materials. John Wiley & Sons, 2011

2011
[88]

Goldman, Handbook of modern ferromagnetic materials

A. Goldman, Handbook of modern ferromagnetic materials. Springer Science & Business Media, 2012, vol. 505

2012
[89]

Anti- ferromagnetic spintronics,

T. Jungwirth, X. Marti, P. Wadley, and J. Wunderlich, “Anti- ferromagnetic spintronics,” Nature nanotechnology, vol. 11, no. 3, pp. 231–241, 2016

2016
[90]

Emerging research landscape of altermagnetism,

L. Šmejkal, J. Sinova, and T. Jungwirth, “Emerging research landscape of altermagnetism,” Phys. Rev. X , vol. 12, p. 040501, Dec 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevX.12.040501

2022 doi
[91]

Beyond conventional ferromagnetism and antiferromagnetism: A phase with nonrelativistic spin and crystal rotation symmetry,

——, “Beyond conventional ferromagnetism and antiferromagnetism: A phase with nonrelativistic spin and crystal rotation symmetry,” Phys. Rev. X , vol. 12, p. 031042, Sep 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevX.12.031042

2022 doi
[92]

Editorial: Altermagnetism—a new punch line of fundamental magnetism,

I. Mazin, “Editorial: Altermagnetism—a new punch line of fundamental magnetism,” Phys. Rev. X , vol. 12, p. 040002, Dec 2022. [Online]. Available: https: //link.aps.org/doi/10.1103/PhysRevX.12.040002

2022 doi
[93]

Momentum- dependent spin splitting by collinear antiferromagnetic ordering,

S. Hayami, Y . Yanagi, and H. Kusunose, “Momentum- dependent spin splitting by collinear antiferromagnetic ordering,” Journal of the Physical Society of Japan , vol. 88, no. 12, p. 123702, 2019. [Online]. Available: https://doi.org/10.7566/JPSJ.88.123702

2019 doi
[94]

Crystal time-reversal symmetry breaking and spontaneous Hall effect in collinear antiferromagnets,

L. ˇSmejkal, R. Gonz ´ alez-Hern´ andez, T. Jungwirth, and J. Sinova, “Crystal time-reversal symmetry breaking and spontaneous Hall effect in collinear antiferromagnets,” Sci. Adv., vol. 6, no. 23, p. eaaz8809, 2020. [Online]. Available: https://www.science.org/doi/abs/10.1126...

2020 doi
[95]

Giant momentum-dependent spin splitting in centrosymmetric low- Z antiferromagnets,

L.-D. Yuan, Z. Wang, J.-W. Luo, E. I. Rashba, and A. Zunger, “Giant momentum-dependent spin splitting in centrosymmetric low- Z antiferromagnets,” Phys. Rev. B, vol. 102, p. 014422, Jul 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.102.014422

2020 doi
[96]

Prediction of unconventional magnetism in doped FeSb 2,

I. I. Mazin, K. Koepernik, M. D. Johannes, R. Gonz ´ alez- Hern´ andez, and L. ˇSmejkal, “Prediction of unconventional magnetism in doped FeSb 2,” Proc. Natl. Acad. Sci. U.S.A. , vol. 118, no. 42, p. e2108924118, 2021. [Online]. Available: https://www.pnas.org/doi/abs/10.1073/...

2021 doi
[97]

Altermagnets and beyond: Nodal magnetically-ordered phases,

T. Jungwirth, R. M. Fernandes, J. Sinova, and L. Smejkal, “Altermagnets and beyond: Nodal magnetically-ordered phases,” arXiv preprint arXiv:2409.10034, 2024

2024 arXiv
[98]

Topological correspondence between magnetic space group representations and subdimensions,

A. Bouhon, G. F. Lange, and R.-J. Slager, “Topological correspondence between magnetic space group representations and subdimensions,” Phys. Rev. B, vol. 103, p. 245127, Jun 2021. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.103.245127

2021 doi
[99]

Hubert and R

A. Hubert and R. Schafer, Magnetic Domains: The Analysis of Magnetic Microstructures , 1st ed. Springer Berlin, Heidelberg, 1998

1998
[100]

Giant and Tunneling Magne- toresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling,

L. ˇSmejkal, A. B. Hellenes, R. González-Hernández, J. Sinova, and T. Jungwirth, “Giant and Tunneling Magne- toresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling,” Phys. Rev. X, vol. 12, p. 011028, Feb 2022. [Online]. Available:...

2022 doi
[101]

Topological superconductivity in two-dimensional altermagnetic metals,

D. Zhu, Z.-Y . Zhuang, Z. Wu, and Z. Yan, “Topological superconductivity in two-dimensional altermagnetic metals,” Phys. Rev. B , vol. 108, p. 184505, Nov 2023. [Online]. Available: 33 https://link.aps.org/doi/10.1103/PhysRevB.108.184505

2023 doi
[102]

Majorana corner modes and tunable patterns in an altermagnet heterostructure,

Y .-X. Li and C.-C. Liu, “Majorana corner modes and tunable patterns in an altermagnet heterostructure,” Phys. Rev. B, vol. 108, p. 205410, Nov 2023. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.108.205410

2023 doi
[103]

Multifunctional antiferromagnetic materials with giant piezomagnetism and noncollinear spin current,

H.-Y . Ma, M. Hu, N. Li, J. Liu, W. Yao, J.-F. Jia, and J. Liu, “Multifunctional antiferromagnetic materials with giant piezomagnetism and noncollinear spin current,” Nat. Commun., vol. 12, p. 2846, MAY 14 2021

2021
[104]

Efficient Electrical Spin Splitter Based on Nonrelativistic Collinear Antiferromagnetism,

R. González-Hernández, L. ˇSmejkal, K. Výborný, Y . Yahagi, J. Sinova, T. c. v. Jungwirth, and J. ˇZelezný, “Efficient Electrical Spin Splitter Based on Nonrelativistic Collinear Antiferromagnetism,” Phys. Rev. Lett. , vol. 126, p. 127701, Mar 2021. [Online]. Available: https:...

2021 doi
[105]

Observation of Spin Splitting Torque in a Collinear Antiferromagnet RuO2,

H. Bai, L. Han, X. Y . Feng, Y . J. Zhou, R. X. Su, Q. Wang, L. Y . Liao, W. X. Zhu, X. Z. Chen, F. Pan, X. L. Fan, and C. Song, “Observation of Spin Splitting Torque in a Collinear Antiferromagnet RuO2,” Phys. Rev. Lett., vol. 128, p. 197202, May 2022. [Online]. Available: ht...

2022 doi
[106]

Observation of Spin-Splitter Torque in Collinear Antiferromagnetic RuO2,

S. Karube, T. Tanaka, D. Sugawara, N. Kadoguchi, M. Kohda, and J. Nitta, “Observation of Spin-Splitter Torque in Collinear Antiferromagnetic RuO2,” Phys. Rev. Lett., vol. 129, p. 137201, Sep 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.129.137201

2022 doi
[107]

Anomalous Hall antiferromagnets,

L. ˇSmejkal, A. H. MacDonald, J. Sinova, S. Nakatsuji, and T. Jungwirth, “Anomalous Hall antiferromagnets,”Nat. Rev. Mater., vol. 7, no. 6, pp. 482–496, JUN 2022

2022
[108]

An anomalous hall effect in altermagnetic ruthenium dioxide,

Z. Feng, X. Zhou, L. ˇSmejkal, L. Wu, Z. Zhu, H. Guo, R. González-Hernández, X. Wang, H. Yan, P. Qin, X. Zhang, H. Wu, H. Chen, Z. Meng, L. Liu, Z. Xia, J. Sinova, T. Jungwirth, and Z. Liu, “An anomalous hall effect in altermagnetic ruthenium dioxide,” Nat. Electron., vol. 5, ...

2022
[109]

Spontaneous Anomalous Hall Effect Arising from an Unconventional Compensated Magnetic Phase in a Semiconductor,

R. D. Gonzalez Betancourt, J. Zubá ˇc, R. Gonzalez- Hernandez, K. Geishendorf, Z. Šobá ˇn, G. Springholz, K. Olejník, L. Šmejkal, J. Sinova, T. Jungwirth, S. T. B. Goennenwein, A. Thomas, H. Reichlová, J. Železný, and D. Kriegner, “Spontaneous Anomalous Hall Effect Arising fro...

2023 doi
[110]

Large intrinsic anomalous Hall effect in both Nb 2FeB2 and Ta 2FeB2 with collinear antiferromagnetism,

X.-Y . Hou, H.-C. Yang, Z.-X. Liu, P.-J. Guo, and Z.-Y . Lu, “Large intrinsic anomalous Hall effect in both Nb 2FeB2 and Ta 2FeB2 with collinear antiferromagnetism,” Phys. Rev. B, vol. 107, p. L161109, Apr 2023. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.10...

2023 doi
[111]

Crystal chirality magneto-optical effects in collinear antiferromagnets,

X. Zhou, W. Feng, X. Yang, G.-Y . Guo, and Y . Yao, “Crystal chirality magneto-optical effects in collinear antiferromagnets,” Phys. Rev. B , vol. 104, p. 024401, Jul
[112]

Crystal Thermal Transport in Altermagnetic RuO 2,

X. Zhou, W. Feng, R.-W. Zhang, L. ˇSmejkal, J. Sinova, Y . Mokrousov, and Y . Yao, “Crystal Thermal Transport in Altermagnetic RuO 2,” Phys. Rev. Lett. , vol. 132, p. 056701, Jan 2024. [Online]. Available: https://link.aps.org/ doi/10.1103/PhysRevLett.132.056701

2024 doi
[113]

Quantum anomalous hall effect in collinear antiferromagnetism,

P.-J. Guo, Z.-X. Liu, and Z.-Y . Lu, “Quantum anomalous hall effect in collinear antiferromagnetism,” npj Comput. Mater., vol. 9, p. 70, MAY 1 2023

2023
[114]

Creation and manipulation of higher-order topological states by altermagnets,

Y .-X. Li, Y . Liu, and C.-C. Liu, “Creation and manipulation of higher-order topological states by altermagnets,” Phys. Rev. B , vol. 109, p. L201109, May 2024. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.109. L201109

2024 doi
[115]

Altermagnetic ferroelectric LiFe 2F6 and spin-triplet excitonic insulator phase,

P.-J. Guo, Y . Gu, Z.-F. Gao, and Z.-Y . Lu, “Altermagnetic ferroelectric LiFe 2F6 and spin-triplet excitonic insulator phase,” 2023

2023
[116]

Extremely strong spin-orbit coupling effect in light element altermagnetic materials,

S. Qu, Z.-F. Gao, H. Sun, K. Liu, P.-J. Guo, and Z.-Y . Lu, “Extremely strong spin-orbit coupling effect in light element altermagnetic materials,” 2024

2024
[117]

Bipolarized weyl semimetals and quantum crystal valley hall effect in two-dimensional altermagnetic materials,

C.-Y . Tan, Z.-F. Gao, H.-C. Yang, K. Liu, P.-J. Guo, and Z.-Y . Lu, “Bipolarized weyl semimetals and quantum crystal valley hall effect in two-dimensional altermagnetic materials,” 2024. [Online]. Available: https: //arxiv.org/abs/2406.16603

2024 arXiv
[118]

Northeast materi- als database (nemad): Enabling discovery of high tran- sition temperature magnetic compounds,

S. Itani, Y . Zhang, and J. Zang, “Northeast materi- als database (nemad): Enabling discovery of high tran- sition temperature magnetic compounds,” arXiv preprint arXiv:2409.15675, 2024

2024
[119]

Gptarticleextractor: An automated workflow for magnetic material database construction,

Y . Zhang, S. Itani, K. Khanal, E. Okyere, G. Smith, K. Taka- hashi, and J. Zang, “Gptarticleextractor: An automated workflow for magnetic material database construction,” Journal of Magnetism and Magnetic Materials , vol. 597, p. 172001, 2024

2024
[120]

The materials project: Accelerating materi- als design through theory-driven data and tools,

A. Jain, J. Montoya, S. Dwaraknath, N. E. Zimmermann, J. Dagdelen, M. Horton, P. Huck, D. Winston, S. Cholia, S. P. Onget al., “The materials project: Accelerating materi- als design through theory-driven data and tools,” Handbook of Materials Modeling: Methods: Theory and Mod...

2020
[121]

D. M. Rowe, Thermoelectrics handbook: macro to nano . CRC press, 2018

2018
[122]

Rationally designing high-performance bulk thermoelectric materials,

G. Tan, L.-D. Zhao, and M. G. Kanatzidis, “Rationally designing high-performance bulk thermoelectric materials,” Chemical reviews , vol. 116, no. 19, pp. 12 123–12 149, 2016

2016
[123]

Thermoelectric generators act as renewable en- ergy sources,

H. Mamur, Ö. F. Dilmaç, J. Begum, and M. R. A. Bhuiyan, “Thermoelectric generators act as renewable en- ergy sources,” Cleaner Materials, vol. 2, p. 100030, 2021

2021
[124]

Af ioffe and origin of modern semiconductor thermoelectric energy conver- sion,

M. Vedernikov and E. Iordanishvili, “Af ioffe and origin of modern semiconductor thermoelectric energy conver- sion,” in Seventeenth International Conference on Thermo- electrics. Proceedings ICT98 (Cat. No. 98TH8365). IEEE, 1998, pp. 37–42

1998
[125]

A practical field guide to thermoelectrics: Fundamentals, synthesis, and characteriza- tion,

A. Zevalkink, D. M. Smiadak, J. L. Blackburn, A. J. Ferguson, M. L. Chabinyc, O. Delaire, J. Wang, K. Kovnir, J. Martin, L. T. Schelhas et al., “A practical field guide to thermoelectrics: Fundamentals, synthesis, and characteriza- tion,” Applied Physics Reviews, vol. 5, no. 2, 2018

2018
[126]

Computation- ally guided discovery of thermoelectric materials,

P. Gorai, V . Stevanovi´c, and E. S. Toberer, “Computation- ally guided discovery of thermoelectric materials,” Nature Reviews Materials, vol. 2, no. 9, pp. 1–16, 2017

2017
[127]

Machine learning assisted discovering of new m2x3- type thermoelectric materials,

D. Chen, F. Jiang, L. Fang, Y .-B. Zhu, C.-C. Ye, and W.-S. Liu, “Machine learning assisted discovering of new m2x3- type thermoelectric materials,” Rare Metals, vol. 41, no. 5, pp. 1543–1553, 2022

2022
[128]

Unsupervised machine learning for discovery of promising half-heusler thermo- 34 electric materials,

X. Jia, Y . Deng, X. Bao, H. Yao, S. Li, Z. Li, C. Chen, X. Wang, J. Mao, F. Cao et al. , “Unsupervised machine learning for discovery of promising half-heusler thermo- 34 electric materials,” npj Computational Materials , vol. 8, no. 1, p. 34, 2022

2022
[129]

Predicting thermoelectric properties from chemical formula with explicitly identify- ing dopant effects,

G. S. Na, S. Jang, and H. Chang, “Predicting thermoelectric properties from chemical formula with explicitly identify- ing dopant effects,” npj Computational Materials , vol. 7, no. 1, p. 106, 2021

2021
[130]

Identify- ing optimal strain in bismuth telluride thermoelectric film by combinatorial gradient thermal annealing and machine learning,

M. Sasaki, S. Ju, Y . Xu, J. Shiomi, and M. Goto, “Identify- ing optimal strain in bismuth telluride thermoelectric film by combinatorial gradient thermal annealing and machine learning,” ACS Combinatorial Science, vol. 22, no. 12, pp. 782–790, 2020

2020
[131]

High-throughput screening for advanced thermoelectric materials: diamond-like abx2 compounds,

R. Li, X. Li, L. Xi, J. Yang, D. J. Singh, and W. Zhang, “High-throughput screening for advanced thermoelectric materials: diamond-like abx2 compounds,” ACS applied materials & interfaces , vol. 11, no. 28, pp. 24 859–24 866, 2019

2019
[132]

Prediction of thermoelectric performance for layered iv-v-vi semiconduc- tors by high-throughput ab initio calculations and machine learning,

Y . Gan, G. Wang, J. Zhou, and Z. Sun, “Prediction of thermoelectric performance for layered iv-v-vi semiconduc- tors by high-throughput ab initio calculations and machine learning,” NPJ Computational Materials , vol. 7, no. 1, p. 176, 2021

2021
[133]

High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity,

M. Islamov, H. Babaei, R. Anderson, K. B. Sezginel, J. R. Long, A. J. McGaughey, D. A. Gomez-Gualdron, and C. E. Wilmer, “High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity,” npj Computational Materials, vol. 9, no. 1, p. 11, 2023

2023
[134]

High- throughput screening of room temperature active peltier cooling materials in heusler compounds,

H. Luo, X. Li, Y . Wang, Y . Jin, M. Yao, and J. Yang, “High- throughput screening of room temperature active peltier cooling materials in heusler compounds,” npj Computa- tional Materials, vol. 8, no. 1, p. 199, 2022

2022
[135]

Closed-loop error-correction learning accelerates experimental discovery of thermoelec- tric materials,

H. Choubisa, M. A. Haque, T. Zhu, L. Zeng, M. Vafaie, D. Baran, and E. H. Sargent, “Closed-loop error-correction learning accelerates experimental discovery of thermoelec- tric materials,” Advanced Materials , vol. 35, no. 40, p. 2302575, 2023

2023
[136]

Helical microtubules of graphitic carbon,

S. Iijima, “Helical microtubules of graphitic carbon,” na- ture, vol. 354, no. 6348, pp. 56–58, 1991

1991
[137]

The rise of graphene,

A. K. Geim and K. S. Novoselov, “The rise of graphene,” Nature materials, vol. 6, no. 3, pp. 183–191, 2007

2007
[138]

Functionalization of mwcnts with ag-aunps by a green method and their catalytic properties,

D. Mendoza-Cachú, J. López-Miranda, C. Mercado- Zúñiga, and G. Rosas, “Functionalization of mwcnts with ag-aunps by a green method and their catalytic properties,” Diamond and Related Materials, vol. 84, pp. 26–31, 2018

2018
[139]

Ballistic carbon nanotube field-effect transistors,

A. Javey, J. Guo, Q. Wang, M. Lundstrom, and H. Dai, “Ballistic carbon nanotube field-effect transistors,” nature, vol. 424, no. 6949, pp. 654–657, 2003

2003
[140]

Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load,

M.-F. Yu, O. Lourie, M. J. Dyer, K. Moloni, T. F. Kelly, and R. S. Ruoff, “Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load,” Science, vol. 287, no. 5453, pp. 637–640, 2000

2000
[141]

Super- capacitors using single-walled carbon nanotube electrodes,

K. H. An, W. S. Kim, Y . S. Park, Y . C. Choi, S. M. Lee, D. C. Chung, D. J. Bae, S. C. Lim, and Y . H. Lee, “Super- capacitors using single-walled carbon nanotube electrodes,” Advanced Materials, vol. 13, no. 7, pp. 497–500, 2001

2001
[142]

Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface,

D. Zhang, P. Yi, X. Lai, L. Peng, and H. Li, “Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface,” Nature Communications, vol. 15, no. 1, p. 344, 2024

2024
[143]

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations,

D. Hedman, B. McLean, C. Bichara, S. Maruyama, J. A. Larsson, and F. Ding, “Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations,” Nature Communications , vol. 15, no. 1, p. 4076, 2024

2024
[144]

Transforming the synthe- sis of carbon nanotubes with machine learning models and automation,

Y . Li, S. Wang, Z. Lv, Z. Wang, Y . Zhao, Y . Xie, Y . Xu, L. Qian, Y . Yang, Z. Zhaoet al., “Transforming the synthe- sis of carbon nanotubes with machine learning models and automation,” arXiv preprint arXiv:2404.01006, 2024

2024 arXiv
[145]

Stochastic full-range multiscale modeling of thermal con- ductivity of polymeric carbon nanotubes composites: A machine learning approach,

B. Liu, N. Vu-Bac, X. Zhuang, X. Fu, and T. Rabczuk, “Stochastic full-range multiscale modeling of thermal con- ductivity of polymeric carbon nanotubes composites: A machine learning approach,” Composite Structures , vol. 289, p. 115393, 2022

2022
[146]

Stochastic multi- scale modeling of electrical conductivity of carbon nan- otube polymer nanocomposites: An interpretable machine learning approach,

M. Elaskalany and K. Behdinan, “Stochastic multi- scale modeling of electrical conductivity of carbon nan- otube polymer nanocomposites: An interpretable machine learning approach,” Advanced Engineering Materials , p. 2401233, 2024

2024
[147]

Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network,

M. Matos, S. Pinho, and V . Tagarielli, “Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network,” Scripta Materialia, vol. 166, pp. 117–121, 2019

2019
[148]

Predicting mechanical properties of carbon nan- otube (cnt) images using multi-layer synthetic finite element model simulations,

K. Safavigerdini, K. Nouduri, R. Surya, A. Reinhard, Z. Quinlan, F. Bunyak, M. R. Maschmann, and K. Pala- niappan, “Predicting mechanical properties of carbon nan- otube (cnt) images using multi-layer synthetic finite element model simulations,” in2023 IEEE International Confe...

2023
[149]

A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities,

V . Papadopoulos, G. Soimiris, D. Giovanis, and M. Pa- padrakakis, “A neural network-based surrogate model for carbon nanotubes with geometric nonlinearities,” Computer Methods in Applied Mechanics and Engineering , vol. 328, pp. 411–430, 2018

2018
[150]

Geometri- cal features can predict electronic properties of graphene nanoflakes,

M. Fernandez, H. Shi, and A. S. Barnard, “Geometri- cal features can predict electronic properties of graphene nanoflakes,” Carbon, vol. 103, pp. 142–150, 2016

2016
[151]

Machine learning electron density in sulfur crosslinked carbon nanotubes,

J. M. Alred, K. V . Bets, Y . Xie, and B. I. Yakobson, “Machine learning electron density in sulfur crosslinked carbon nanotubes,” Composites Science and Technology , vol. 166, pp. 3–9, 2018

2018
[152]

Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction,

A. V . Singh, D. Rosenkranz, M. H. D. Ansari, R. Singh, A. Kanase, S. P. Singh, B. Johnston, J. Tentschert, P. Laux, and A. Luch, “Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction,” Advanced Intelligent Systems, vo...

2020
[153]

2d materials and van der waals heterostructures,

K. Novoselov, A. Mishchenko, A. Carvalho, and A. Cas- tro Neto, “2d materials and van der waals heterostructures,” Science, vol. 353, no. 6298, p. aac9439, 2016

2016
[154]

The space group classification of topological band-insulators,

R.-J. Slager, A. Mesaros, V . Juri ˇci´c, and J. Zaanen, “The space group classification of topological band-insulators,” Nature Physics , vol. 9, pp. 98–102, Feb 2013. [Online]. Available: https://doi.org/10.1038/nphys2513

2013 doi
[155]

Topological classification of crystalline insulators through band structure combinatorics,

J. Kruthoff, J. de Boer, J. van Wezel, C. L. Kane, and R.-J. Slager, “Topological classification of crystalline insulators through band structure combinatorics,” Phys. Rev. X, vol. 7, p. 041069, Dec 2017. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevX.7.041069

2017 doi
[156]

Symmetry- based indicators of band topology in the 230 space groups,

H. C. Po, A. Vishwanath, and H. Watanabe, “Symmetry- based indicators of band topology in the 230 space groups,” Nature communications, vol. 8, no. 1, p. 50, 2017

2017
[157]

Topological 35 quantum chemistry,

B. Bradlyn, L. Elcoro, J. Cano, M. G. Vergniory, Z. Wang, C. Felser, M. I. Aroyo, and B. A. Bernevig, “Topological 35 quantum chemistry,” Nature, vol. 547, no. 7663, pp. 298– 305, 2017

2017
[158]

A complete catalogue of high- quality topological materials,

M. Vergniory, L. Elcoro, C. Felser, N. Regnault, B. A. Bernevig, and Z. Wang, “A complete catalogue of high- quality topological materials,” Nature, vol. 566, no. 7745, pp. 480–485, 2019

2019
[159]

Catalogue of topological electronic materials,

T. Zhang, Y . Jiang, Z. Song, H. Huang, Y . He, Z. Fang, H. Weng, and C. Fang, “Catalogue of topological electronic materials,” Nature, vol. 566, no. 7745, pp. 475–479, 2019

2019
[160]

High-throughput computational discovery of in2mn2o7 as a high curie temperature ferromagnetic semi- conductor for spintronics,

W. Chen, J. George, J. B. Varley, G.-M. Rignanese, and G. Hautier, “High-throughput computational discovery of in2mn2o7 as a high curie temperature ferromagnetic semi- conductor for spintronics,” npj Computational Materials , vol. 5, no. 1, p. 72, 2019

2019
[161]

High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using density func- tional theory,

M. K. Horton, J. H. Montoya, M. Liu, and K. A. Persson, “High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using density func- tional theory,” npj Computational Materials , vol. 5, no. 1, p. 64, 2019

2019
[162]

Graphene: status and prospects,

A. K. Geim, “Graphene: status and prospects,” science, vol. 324, no. 5934, pp. 1530–1534, 2009

2009
[163]

2d transition metal dichalcogenides,

S. Manzeli, D. Ovchinnikov, D. Pasquier, O. V . Yazyev, and A. Kis, “2d transition metal dichalcogenides,” Nature Reviews Materials, vol. 2, no. 8, pp. 1–15, 2017

2017
[164]

Interlayer coupling: an additional degree of freedom in two-dimensional materials,

S. Pei, Z. Wang, and J. Xia, “Interlayer coupling: an additional degree of freedom in two-dimensional materials,” ACS nano, vol. 16, no. 8, pp. 11 498–11 503, 2022

2022
[165]

Topological crystalline insulators from stacked graphene layers,

S. K. Das, B. Yan, J. van den Brink, and I. C. Fulga, “Topological crystalline insulators from stacked graphene layers,” Physical Review B, vol. 99, no. 16, p. 165418, 2019

2019
[166]

A review on mechanical exfoliation for the scalable production of graphene,

M. Yi and Z. Shen, “A review on mechanical exfoliation for the scalable production of graphene,” Journal of Materials Chemistry A, vol. 3, no. 22, pp. 11 700–11 715, 2015

2015
[167]

Review of chemical vapor deposition of graphene and related applications,

Y . Zhang, L. Zhang, and C. Zhou, “Review of chemical vapor deposition of graphene and related applications,” Accounts of chemical research , vol. 46, no. 10, pp. 2329– 2339, 2013

2013
[168]

Machine learning-assisted synthesis of two-dimensional materials,

M. Lu, H. Ji, Y . Zhao, Y . Chen, J. Tao, Y . Ou, Y . Wang, Y . Huang, J. Wang, and G. Hao, “Machine learning-assisted synthesis of two-dimensional materials,” ACS Applied Ma- terials & Interfaces, vol. 15, no. 1, pp. 1871–1878, 2022

2022
[169]

Understanding, discovery, and synthesis of 2d materials enabled by machine learning,

B. Ryu, L. Wang, H. Pu, M. K. Chan, and J. Chen, “Understanding, discovery, and synthesis of 2d materials enabled by machine learning,” Chemical Society Reviews , vol. 51, no. 6, pp. 1899–1925, 2022

1925
[170]

Colloquium: topological insulators,

M. Z. Hasan and C. L. Kane, “Colloquium: topological insulators,” Reviews of modern physics , vol. 82, no. 4, pp. 3045–3067, 2010

2010
[171]

Topological insulator materials,

Y . Ando, “Topological insulator materials,” Journal of the Physical Society of Japan, vol. 82, no. 10, p. 102001, 2013

2013
[172]

Machine learn- ing for materials discovery: Two-dimensional topological insulators,

G. R. Schleder, B. Focassio, and A. Fazzio, “Machine learn- ing for materials discovery: Two-dimensional topological insulators,” Applied Physics Reviews, vol. 8, no. 3, 2021

2021
[173]

High-throughput identification and characterization of two-dimensional materials using density functional theory,

K. Choudhary, I. Kalish, R. Beams, and F. Tavazza, “High-throughput identification and characterization of two-dimensional materials using density functional theory,” Scientific reports, vol. 7, no. 1, p. 5179, 2017

2017
[174]

Two-dimensional materials from high-throughput computational exfoliation of experimen- tally known compounds,

N. Mounet, M. Gibertini, P. Schwaller, D. Campi, A. Merkys, A. Marrazzo, T. Sohier, I. E. Castelli, A. Ce- pellotti, G. Pizzi et al. , “Two-dimensional materials from high-throughput computational exfoliation of experimen- tally known compounds,” Nature nanotechnology, vol. 13...

2018
[175]

Effi- cient computational design of 2d van der waals heterostruc- tures: Band-alignment,

K. Choudhary, K. Garrity, G. Pilania, and F. Tavazza, “Effi- cient computational design of 2d van der waals heterostruc- tures: Band-alignment,” Lattice-Mismatch, Web-app Gener- ation and Machinelearning. arXiv, 2004

2004
[176]

Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation,

S. B. Harris, A. Biswas, S. J. Yun, K. M. Roccapriore, C. M. Rouleau, A. A. Puretzky, R. K. Vasudevan, D. B. Geohegan, and K. Xiao, “Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation,” Small Methods, p. 2301...

2024
[177]

Unsuper- vised discovery of thin-film photovoltaic materials from unlabeled data,

Z. Wang, J. Cai, Q. Wang, S. Wu, and J. Li, “Unsuper- vised discovery of thin-film photovoltaic materials from unlabeled data,” npj Computational Materials, vol. 7, no. 1, p. 128, 2021

2021
[178]

Prediction of synthesis of 2d metal carbides and nitrides (mxenes) and their precursors with positive and unlabeled machine learning,

N. C. Frey, J. Wang, G. I. Vega Bellido, B. Anasori, Y . Gogotsi, and V . B. Shenoy, “Prediction of synthesis of 2d metal carbides and nitrides (mxenes) and their precursors with positive and unlabeled machine learning,” ACS nano, vol. 13, no. 3, pp. 3031–3041, 2019

2019
[179]

Reinforced active learning for cvd-grown two-dimensional materials characterization,

Z. Li, F. Yao, and H. Sun, “Reinforced active learning for cvd-grown two-dimensional materials characterization,” IISE Transactions, vol. 56, no. 8, pp. 811–823, 2024

2024
[180]

Quantum material synthesis by reinforcement learning,

P. Rajak, A. Krishnamoorthy, R. Kalia, A. Nakano, and P. Vashishta, “Quantum material synthesis by reinforcement learning,” in Proceedings of NeurIPS Workshop on Machine Learning and the Physical Sciences, 2020, p. 170

2020
[181]

Machine learning driven synthesis of few-layered wte2 with geometrical control,

M. Xu, B. Tang, Y . Lu, C. Zhu, Q. Lu, C. Zhu, L. Zheng, J. Zhang, N. Han, W. Fang et al., “Machine learning driven synthesis of few-layered wte2 with geometrical control,” Journal of the American Chemical Society, vol. 143, no. 43, pp. 18 103–18 113, 2021

2021
[182]

Machine learning- guided synthesis of advanced inorganic materials,

B. Tang, Y . Lu, J. Zhou, T. Chouhan, H. Wang, P. Golani, M. Xu, Q. Xu, C. Guan, and Z. Liu, “Machine learning- guided synthesis of advanced inorganic materials,” Materi- als Today, vol. 41, pp. 72–80, 2020

2020
[183]

The computational 2d materials database: high-throughput modeling and discov- ery of atomically thin crystals,

S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P. S. Schmidt, N. F. Hinsche, M. N. Gjerding, D. Torelli, P. M. Larsen, A. C. Riis-Jensen et al. , “The computational 2d materials database: high-throughput modeling and discov- ery of atomically thin crystals,” 2D Materials, vo...

2018
[184]

Recent progress of the computational 2d materials database (c2db),

M. N. Gjerding, A. Taghizadeh, A. Rasmussen, S. Ali, F. Bertoldo, T. Deilmann, N. R. Knøsgaard, M. Kruse, A. H. Larsen, S. Manti et al. , “Recent progress of the computational 2d materials database (c2db),” 2D Materials, vol. 8, no. 4, p. 044002, 2021

2021
[185]

2dmat- pedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches,

J. Zhou, L. Shen, M. D. Costa, K. A. Persson, S. P. Ong, P. Huck, Y . Lu, X. Ma, Y . Chen, H. Tang et al. , “2dmat- pedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches,” Sci- entific data, vol. 6, no. 1, p. 86, 2019

2019
[186]

Materials cloud, a platform for open computational science,

L. Talirz, S. Kumbhar, E. Passaro, A. V . Yakutovich, V . Granata, F. Gargiulo, M. Borelli, M. Uhrin, S. P. Huber, S. Zoupanos et al. , “Materials cloud, a platform for open computational science,” Scientific data, vol. 7, no. 1, p. 299, 2020

2020
[187]

Expansion of the materials cloud 2d database,

D. Campi, N. Mounet, M. Gibertini, G. Pizzi, and N. Marzari, “Expansion of the materials cloud 2d database,” ACS nano, vol. 17, no. 12, pp. 11 268–11 278, 2023

2023
[188]

Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predicted by machine learning,

E. I. Marchenko, S. A. Fateev, A. A. Petrov, V . V . Korolev, 36 A. Mitrofanov, A. V . Petrov, E. A. Goodilin, and A. B. Tarasov, “Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predi...

2020
[189]

Machine learning-enabled design of point defects in 2d materials for quantum and neuromorphic information pro- cessingfrey2020machine,

N. C. Frey, D. Akinwande, D. Jariwala, and V . B. Shenoy, “Machine learning-enabled design of point defects in 2d materials for quantum and neuromorphic information pro- cessingfrey2020machine,” ACS nano, vol. 14, no. 10, pp. 13 406–13 417, 2020

2020
[190]

Small data machine learn- ing in materials science,

P. Xu, X. Ji, M. Li, and W. Lu, “Small data machine learn- ing in materials science,” npj Computational Materials , vol. 9, no. 1, p. 42, 2023

2023
[191]

Network analysis and natural language processing to obtain a landscape of the scientific literature on materials applications,

A. C. M. Brito, M. C. F. Oliveira, O. N. Oliveira Jr, F. N. Silva, and D. R. Amancio, “Network analysis and natural language processing to obtain a landscape of the scientific literature on materials applications,”ACS Applied Materials & Interfaces, vol. 15, no. 23, pp. 27 437...

2023
[192]

Solar cell efficiency tables (version 64),

M. A. Green, E. D. Dunlop, M. Yoshita, N. Kopidakis, K. Bothe, G. Siefer, D. Hinken, M. Rauer, J. Hohl- Ebinger, and X. Hao, “Solar cell efficiency tables (version 64),” Progress in Photovoltaics: Research and Applications, vol. 32, no. 7, pp. 425–441, 2024

2024
[193]

Photovoltaic materials: Present efficiencies and fu- ture challenges,

A. Polman, M. Knight, E. C. Garnett, B. Ehrler, and W. C. Sinke, “Photovoltaic materials: Present efficiencies and fu- ture challenges,” Science, vol. 352, no. 6283, p. aad4424, 2016

2016
[194]

The physics of solar cells,

J. Nelson, “The physics of solar cells,” Imperial College Press google schola, vol. 2, pp. 62–68, 2003

2003
[195]

Photovoltaics report fraun- hofer institute for solar energy systems,

S. Philipps and W. Warmuth, “Photovoltaics report fraun- hofer institute for solar energy systems,” ISE with Support of PSE GmbH November 14th; Fraunhofer ISE: Freiburg, Germany, 2019

2019
[196]

B. G. Streetman, S. Banerjee et al. , Solid state electronic devices. Prentice hall New Jersey, 2000, vol. 4

2000
[197]

Nakamura and G

S. Nakamura and G. Fasol, The blue laser diode: GaN based light emitters and lasers . Springer Science & Business Media, 2013

2013
[198]

M. E. Levinshtein, S. L. Rumyantsev, and M. S. Shur, Properties of Advanced Semiconductor Materials: GaN, AIN, InN, BN, SiC, SiGe. John Wiley & Sons, 2001

2001
[199]

A brief review of high efficiency iii-v solar cells for space application,

J. Li, A. Aierken, Y . Liu, Y . Zhuang, X. Yang, J. Mo, R. Fan, Q. Chen, S. Zhang, Y . Huanget al., “A brief review of high efficiency iii-v solar cells for space application,” Frontiers in Physics, vol. 8, p. 631925, 2021

2021
[200]

High-efficiency multijunction solar cells,

F. Dimroth and S. Kurtz, “High-efficiency multijunction solar cells,”MRS bulletin, vol. 32, no. 3, pp. 230–235, 2007

2007
[201]

Organometal halide perovskites as visible-light sensitizers for photovoltaic cells,

A. Kojima, K. Teshima, Y . Shirai, and T. Miyasaka, “Organometal halide perovskites as visible-light sensitizers for photovoltaic cells,” Journal of the american chemical society, vol. 131, no. 17, pp. 6050–6051, 2009

2009
[202]

Perovskites: the emergence of a new era for low-cost, high-efficiency solar cells,

H. J. Snaith, “Perovskites: the emergence of a new era for low-cost, high-efficiency solar cells,” The journal of physical chemistry letters , vol. 4, no. 21, pp. 3623–3630, 2013

2013
[203]

R. J. Tilley, Perovskites: structure-property relationships . John Wiley & Sons, 2016

2016
[204]

Machine learning for per- ovskite materials design and discovery,

Q. Tao, P. Xu, M. Li, and W. Lu, “Machine learning for per- ovskite materials design and discovery,” Npj computational materials, vol. 7, no. 1, p. 23, 2021

2021
[205]

Perovskite Solar Cells: A Review of the Recent Advances,

P. Roy, A. Ghosh, F. Barclay, A. Khare, and E. Cuce, “Perovskite Solar Cells: A Review of the Recent Advances,” Coatings, vol. 12, no. 8, p. 1089, Jul
[206]

Machine learning as a characterization method for analysis and design of perovskite solar cells,

Y . Liu, X. Tan, P. Xiang, Y . Tu, T. Shao, Y . Zang, X. Li, and W. Yan, “Machine learning as a characterization method for analysis and design of perovskite solar cells,” Materials Today Physics , vol. 42, p. 101359, Mar
[207]

Comprehensive Machine Learning Pipeline for Prediction of Power Conversion Efficiency in Perovskite Solar Cells,

D. Mohanty and A. K. Palai, “Comprehensive Machine Learning Pipeline for Prediction of Power Conversion Efficiency in Perovskite Solar Cells,” Advanced Theory and Simulations , vol. 6, no. 12, p. 2300309, Dec. 2023. [Online]. Available: https://onlinelibrary.wiley.com/doi/10. ...

2023
[208]

Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach,

B. Akbar, H. Tayara, and K. T. Chong, “Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach,” iScience, vol. 27, no. 3, p. 109200, Mar
[209]

Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites,

N. Karimitari, W. J. Baldwin, E. W. Muller, Z. J. L. Bare, W. J. Kennedy, G. Csányi, and C. Sutton, “Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites,” Journal of the American Chemical Society , vol. 146, no. 40, pp. 27 392–27 404, Oct. 2024...

2024 doi
[210]

Halide Vacancies Create No Charge Traps on Lead Halide Perovskite Surfaces but Can Generate Deep Traps in the Bulk,

J. Ran, B. Wang, Y . Wu, D. Liu, C. Mora Perez, A. S. Vasenko, and O. V . Prezhdo, “Halide Vacancies Create No Charge Traps on Lead Halide Perovskite Surfaces but Can Generate Deep Traps in the Bulk,” The Journal of Physical Chemistry Letters , vol. 14, no. 26, pp. 6028–6036, ...

2023 doi
[211]

The influence of perovskite crystal structure on its stability,

H. Bi, M. Wang, L. Liu, J. Yan, R. Zeng, Z. Xu, and J. Wang, “The influence of perovskite crystal structure on its stability,” Journal of Materials Chemistry A , vol. 12, no. 21, pp. 12 744–12 751, 2024. [Online]. Available: https://xlink.rsc.org/?DOI=D3TA07457A

2024
[212]

Universal machine learning aided synthesis approach of two-dimensional per- ovskites in a typical laboratory,

Y . Wu, C.-F. Wang, M.-G. Ju, Q. Jia, Q. Zhou, S. Lu, X. Gao, Y . Zhang, and J. Wang, “Universal machine learning aided synthesis approach of two-dimensional per- ovskites in a typical laboratory,” Nature Communications, vol. 15, no. 1, p. 138, 2024

2024
[213]

Data driven high quantum yield halide perovskite phosphors design and fabrication,

H. Mai, X. Wen, X. Li, N. S. Dissanayake, X. Sun, Y . Lu, T. C. Le, S. P. Russo, D. Chen, D. A. Winkler et al., “Data driven high quantum yield halide perovskite phosphors design and fabrication,” Materials Today, vol. 74, pp. 12– 21, 2024

2024
[214]

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations,

X. Li, H. Mai, J. Lu, X. Wen, T. C. Le, S. P. Russo, D. A. Winkler, D. Chen, and R. A. Caruso, “Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations,” Angewandte Chemie International Edition , vo...

2023 doi
[215]

Available: https://linkinghub.elsevier.com/ retrieve/pii/S2589004224004218

[Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S2589004224004218
[216]

Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees,

B. Selvaratnam, A. O. Oliynyk, and A. Mar, “Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees,” Inorganic Chemistry , vol. 62, no. 28, pp. 10 865–10 875, J...

2023 doi
[217]

Advances in organic photo- voltaic cells: A comprehensive review of materials, tech- nologies, and performance,

E. K. Solak and E. Irmak, “Advances in organic photo- voltaic cells: A comprehensive review of materials, tech- nologies, and performance,” RSC advances, vol. 13, no. 18, pp. 12 244–12 269, 2023

2023
[218]

Polymer–fullerene bulk-heterojunction solar cells,

C. J. Brabec, S. Gowrisanker, J. J. Halls, D. Laird, S. Jia, and S. P. Williams, “Polymer–fullerene bulk-heterojunction solar cells,” Advanced Materials, vol. 22, no. 34, pp. 3839– 3856, 2010

2010
[219]

Photoinduced Electron Transfer from a Conducting Polymer to Buckminsterfullerene,

N. S. Sariciftci, L. Smilowitz, A. J. Heeger, and F. Wudl, “Photoinduced Electron Transfer from a Conducting Polymer to Buckminsterfullerene,” Science, vol. 258, no. 5087, pp. 1474–1476, Nov. 1992. [Online]. Available: https: //www.science.org/doi/10.1126/science.258.5087.1474

1992
[220]

Polymer solar cells,

G. Li, R. Zhu, and Y . Yang, “Polymer solar cells,” Nature photonics, vol. 6, no. 3, pp. 153–161, 2012

2012
[221]

Single-Junction Organic Solar Cell with over 15% Efficiency Using Fused- Ring Acceptor with Electron-Deficient Core,

J. Yuan, Y . Zhang, L. Zhou, G. Zhang, H.-L. Yip, T.-K. Lau, X. Lu, C. Zhu, H. Peng, P. A. Johnson, M. Leclerc, Y . Cao, J. Ulanski, Y . Li, and Y . Zou, “Single-Junction Organic Solar Cell with over 15% Efficiency Using Fused- Ring Acceptor with Electron-Deficient Core,” Joul...

2019
[222]

Interpretable discovery of semiconductors with machine learning,

H. Choubisa, P. Todorovi ´c, J. M. Pina, D. H. Parmar, Z. Li, O. V oznyy, I. Tamblyn, and E. H. 37 Sargent, “Interpretable discovery of semiconductors with machine learning,” npj Computational Materials , vol. 9, no. 1, p. 117, Jun. 2023. [Online]. Available: https://www.natur...

2023
[223]

High-Efficiency Non-Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry,

Q. Zhang, Y . J. Zheng, W. Sun, Z. Ou, O. Odunmbaku, M. Li, S. Chen, Y . Zhou, J. Li, B. Qin, and K. Sun, “High-Efficiency Non-Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry,” Advanced Science, vol. 9, no. 6, p. 2104742, Feb. 2022. [Online]. Available:...

2022
[224]

Machine learning approach for predicting high JSC donor molecules in fullerene- typed organic solar cells,

Y . Morishita, M. Yarimizu, M. Kaneko, and A. Muraoka, “Machine learning approach for predicting high JSC donor molecules in fullerene- typed organic solar cells,” Chemical Physics Letters , p. 141719, Oct. 2024. [Online]. Available: https: //linkinghub.elsevier.com/retrieve/p...

2024
[225]

Accelerating the discovery of high-performance donor/ac- ceptor pairs in photovoltaic materials via machine learning and density functional theory,

X. Liu, Y . Shao, T. Lu, D. Chang, M. Li, and W. Lu, “Accelerating the discovery of high-performance donor/ac- ceptor pairs in photovoltaic materials via machine learning and density functional theory,” Materials & Design , vol. 216, p. 110561, Apr. 2022. [Online]. Available: ...

2022
[226]

Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells,

R. Suthar, T. Abhijith, P. Sharma, and S. Karak, “Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells,” Solar Energy , vol. 250, pp. 119–127, Jan
[227]

Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells,

M. Li, C. Zhang, M. Zhang, J. Gong, X. Liu, Y . Chen, Z. Liu, Y . Wu, and H. Chen, “Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells,” physica status solidi (a) , vol. 221, no. 9, p. 2400008, May 2024. [Online]. Available: https: ...

2024 doi
[228]

Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors,

R. Suthar, A. T, and S. Karak, “Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors,” Journal of Materials Chemistry A , vol. 11, no. 41, pp. 22 248–22 258, 2023. [Online]. Availab...

2023
[229]

Advances in organic photovoltaic cells: a comprehensive review of materials, technologies, and performance,

E. K. Solak and E. Irmak, “Advances in organic photovoltaic cells: a comprehensive review of materials, technologies, and performance,” RSC Advances , vol. 13, no. 18, pp. 12 244–12 269, 2023. [Online]. Available: https://xlink.rsc.org/?DOI=D3RA01454A

2023
[230]

Efficient screening framework for organic solar cells with deep learning and ensemble learning,

H. Wang, J. Feng, Z. Dong, L. Jin, M. Li, J. Yuan, and Y . Li, “Efficient screening framework for organic solar cells with deep learning and ensemble learning,” npj Computational Materials , vol. 9, no. 1, p. 200, Oct
[231]

Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing,

P. Shetty, A. Adeboye, S. Gupta, C. Zhang, and R. Ramprasad, “Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing,” Chemistry of Materials , p. acs.chemmater.4c00709, Aug. 2024. [Online]. Available: https://pubs...

2024 doi
[232]

Machine learning descriptors for data-driven catalysis study,

L.-H. Mou, T. Han, P. E. Smith, E. Sharman, and J. Jiang, “Machine learning descriptors for data-driven catalysis study,” Advanced Science , vol. 10, no. 22, p. 2301020, 2023

2023
[233]

Machine-learning- guided discovery and optimization of additives in preparing cu catalysts for co2 reduction,

Y . Guo, X. He, Y . Su, Y . Dai, M. Xie, S. Yang, J. Chen, K. Wang, D. Zhou, and C. Wang, “Machine-learning- guided discovery and optimization of additives in preparing cu catalysts for co2 reduction,” Journal of the American Chemical Society, vol. 143, no. 15, pp. 5755–5762, 2021

2021
[234]

Available: https://linkinghub.elsevier.com/ retrieve/pii/S0038092X22009008

[Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0038092X22009008
[235]

Beyond scaling relations for the description of catalytic materials,

M. Andersen, S. V . Levchenko, M. Scheffler, and K. Reuter, “Beyond scaling relations for the description of catalytic materials,” Acs Catalysis , vol. 9, no. 4, pp. 2752–2759, 2019

2019
[236]

Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates,

R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, and L. M. Ghiringhelli, “Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates,” Physical Review Materi- als, vol. 2, no. 8, p. 083802, 2018

2018
[237]

Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data,

Y . Miyake, K. Kranthiraja, F. Ishiwari, and A. Saeki, “Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data,” Chemistry of Materials , vol. 34, no. 15, pp. 6912–6920, Aug. 2022. [Online]. Ava...

2022 doi
[238]

Data-driven descrip- 38 tor engineering and refined scaling relations for predicting transition metal oxide reactivity,

W. Xu, M. Andersen, and K. Reuter, “Data-driven descrip- 38 tor engineering and refined scaling relations for predicting transition metal oxide reactivity,” ACS Catalysis , vol. 11, no. 2, pp. 734–742, 2020

2020
[239]

Available: https://www.nature.com/articles/ s41524-023-01155-9

[Online]. Available: https://www.nature.com/articles/ s41524-023-01155-9
[240]

A universal descriptor for complicated in- terfacial effects on electrochemical reduction reactions,

C. Ren, S. Lu, Y . Wu, Y . Ouyang, Y . Zhang, Q. Li, C. Ling, and J. Wang, “A universal descriptor for complicated in- terfacial effects on electrochemical reduction reactions,” Journal of the American Chemical Society, vol. 144, no. 28, pp. 12 874–12 883, 2022

2022
[241]

High-throughput screening of bimetallic catalysts enabled by machine learning,

Z. Li, S. Wang, W. S. Chin, L. E. Achenie, and H. Xin, “High-throughput screening of bimetallic catalysts enabled by machine learning,” Journal of Materials Chemistry A , vol. 5, no. 46, pp. 24 131–24 138, 2017

2017
[242]

An adaptive machine learning strategy for accelerating discovery of perovskite electrocatalysts,

Z. Li, L. E. Achenie, and H. Xin, “An adaptive machine learning strategy for accelerating discovery of perovskite electrocatalysts,” ACS Catalysis, vol. 10, no. 7, pp. 4377– 4384, 2020

2020
[243]

Designing catalyst descrip- tors for machine learning in oxidative coupling of methane,

S. Ishioka, A. Fujiwara, S. Nakanowatari, L. Takahashi, T. Taniike, and K. Takahashi, “Designing catalyst descrip- tors for machine learning in oxidative coupling of methane,” ACS Catalysis, vol. 12, no. 19, pp. 11 541–11 546, 2022

2022
[244]

Electric dipole descriptor for machine learning prediction of catalyst surface–molecular adsorbate interactions,

X. Wang, S. Ye, W. Hu, E. Sharman, R. Liu, Y . Liu, Y . Luo, and J. Jiang, “Electric dipole descriptor for machine learning prediction of catalyst surface–molecular adsorbate interactions,” Journal of the American Chemical Society , vol. 142, no. 17, pp. 7737–7743, 2020

2020
[245]

Electronic spin moment as a catalytic descriptor for fe single-atom catalysts supported on c2n,

W. Zhong, Y . Qiu, H. Shen, X. Wang, J. Yuan, C. Jia, S. Bi, and J. Jiang, “Electronic spin moment as a catalytic descriptor for fe single-atom catalysts supported on c2n,” Journal of the American Chemical Society, vol. 143, no. 11, pp. 4405–4413, 2021

2021
[246]

New tolerance factor to predict the stability of perovskite oxides and halides,

C. J. Bartel, C. Sutton, B. R. Goldsmith, R. Ouyang, C. B. Musgrave, L. M. Ghiringhelli, and M. Scheffler, “New tolerance factor to predict the stability of perovskite oxides and halides,” Science advances, vol. 5, no. 2, p. eaav0693, 2019

2019
[247]

En- abling catalyst discovery through machine learning and high-throughput experimentation,

T. Williams, K. McCullough, and J. A. Lauterbach, “En- abling catalyst discovery through machine learning and high-throughput experimentation,” Chemistry of Materials, vol. 32, no. 1, pp. 157–165, 2019

2019
[248]

Bridging the complexity gap in computational heterogeneous catalysis with machine learning,

T. Mou, H. S. Pillai, S. Wang, M. Wan, X. Han, N. M. Schweitzer, F. Che, and H. Xin, “Bridging the complexity gap in computational heterogeneous catalysis with machine learning,” Nature Catalysis , vol. 6, no. 2, pp. 122–136, 2023

2023
[249]

Predicting the activity and selectivity of bimetallic metal catalysts for ethanol reforming using machine learning,

N. Artrith, Z. Lin, and J. G. Chen, “Predicting the activity and selectivity of bimetallic metal catalysts for ethanol reforming using machine learning,” Acs Catalysis, vol. 10, no. 16, pp. 9438–9444, 2020

2020
[250]

An all-round ai- chemist with a scientific mind,

Q. Zhu, F. Zhang, Y . Huang, H. Xiao, L. Zhao, X. Zhang, T. Song, X. Tang, X. Li, G. He et al. , “An all-round ai- chemist with a scientific mind,” National Science Review , vol. 9, no. 10, p. nwac190, 2022

2022
[251]

Accelerated discovery of co2 electrocatalysts using active machine learning,

M. Zhong, K. Tran, Y . Min, C. Wang, Z. Wang, C.-T. Dinh, P. De Luna, Z. Yu, A. S. Rasouli, P. Brodersen et al. , “Accelerated discovery of co2 electrocatalysts using active machine learning,” Nature, vol. 581, no. 7807, pp. 178–183, 2020

2020
[252]

Neural network- assisted development of high-entropy alloy catalysts: de- coupling ligand and coordination effects,

Z. Lu, Z. W. Chen, and C. V . Singh, “Neural network- assisted development of high-entropy alloy catalysts: de- coupling ligand and coordination effects,” Matter, vol. 3, no. 4, pp. 1318–1333, 2020

2020
[253]

Machine-learning-accelerated design of high-performance platinum intermetallic nanopar- ticle fuel cell catalysts,

P. Yin, X. Niu, S.-B. Li, K. Chen, X. Zhang, M. Zuo, L. Zhang, and H.-W. Liang, “Machine-learning-accelerated design of high-performance platinum intermetallic nanopar- ticle fuel cell catalysts,” Nature Communications, vol. 15, no. 1, p. 415, 2024

2024
[254]

A universal machine learning framework for electrocatalyst innovation: a case study of discovering al- loys for hydrogen evolution reaction,

L. Chen, Y . Tian, X. Hu, S. Yao, Z. Lu, S. Chen, X. Zhang, and Z. Zhou, “A universal machine learning framework for electrocatalyst innovation: a case study of discovering al- loys for hydrogen evolution reaction,”Advanced Functional Materials, vol. 32, no. 47, p. 2208418, 2022

2022
[255]

Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features,

Y . Gu, Q. Zhu, Z. Liu, C. Fu, J. Wu, Q. Zhu, Q. Jia, and J. Ma, “Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features,” Journal of Materials Chemistry A , vol. 10, no...

2022
[256]

Nanostructured high- entropy alloys with multiple principal elements: novel alloy design concepts and outcomes,

J.-W. Yeh, S.-K. Chen, S.-J. Lin, J.-Y . Gan, T.-S. Chin, T.-T. Shun, C.-H. Tsau, and S.-Y . Chang, “Nanostructured high- entropy alloys with multiple principal elements: novel alloy design concepts and outcomes,” Advanced engineering ma- terials, vol. 6, no. 5, pp. 299–303, 2004

2004
[257]

Coupling high-throughput experiments and regression al- gorithms to optimize pgm-free orr electrocatalyst synthe- sis,

M. R. Karim, M. Ferrandon, S. Medina, E. Sture, N. Kar- iuki, D. J. Myers, E. F. Holby, P. Zelenay, and T. Ahmed, “Coupling high-throughput experiments and regression al- gorithms to optimize pgm-free orr electrocatalyst synthe- sis,” ACS Applied Energy Materials, vol. 3, no. ...

2020
[258]

High-entropy alloys,

E. P. George, D. Raabe, and R. O. Ritchie, “High-entropy alloys,” Nature reviews materials , vol. 4, no. 8, pp. 515– 534, 2019

2019
[259]

Relative effects of enthalpy and entropy on the phase stability of equiatomic high-entropy alloys,

F. Otto, Y . Yang, H. Bei, and E. George, “Relative effects of enthalpy and entropy on the phase stability of equiatomic high-entropy alloys,” Acta Materialia , vol. 61, no. 7, pp. 2628–2638, 2013. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S135964...

2013
[260]

Machine learning–enabled high-entropy alloy discovery,

Z. Rao, P.-Y . Tung, R. Xie, Y . Wei, H. Zhang, A. Ferrari, T. Klaver, F. Körmann, P. T. Sukumar, A. Kwiatkowski da Silva et al., “Machine learning–enabled high-entropy alloy discovery,”Science, vol. 378, no. 6615, pp. 78–85, 2022

2022
[261]

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence,

Z.-K. Han, D. Sarker, R. Ouyang, A. Mazheika, Y . Gao, and S. V . Levchenko, “Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence,” Nature communications, vol. 12, no. 1, p. 1833, 2021

2021
[262]

A neural network model for high entropy alloy design,

J. Wang, H. Kwon, H. S. Kim, and B.-J. Lee, “A neural network model for high entropy alloy design,” npj Compu- tational Materials, vol. 9, no. 1, p. 60, 2023

2023
[263]

Support vector machine-based phase prediction of multi- principal element alloys,

N. H. Chau, M. Kubo, L. V . Hai, and T. Yamamoto, “Support vector machine-based phase prediction of multi- principal element alloys,” Vietnam Journal of Computer Science, vol. 10, no. 01, pp. 101–116, 2023

2023
[264]

Generative language model for catalyst discovery,

D. H. Mok and S. Back, “Generative language model for catalyst discovery,” 2024. [Online]. Available: https://arxiv.org/abs/2407.14040

2024 arXiv
[265]

Machine learning approach to predict new multiphase high entropy alloys,

Y . V . Krishna, U. K. Jaiswal, and M. Rahul, “Machine learning approach to predict new multiphase high entropy alloys,” Scripta Materialia, vol. 197, p. 113804, 2021

2021
[266]

Mi- crostructural development in equiatomic multicomponent alloys,

B. Cantor, I. Chang, P. Knight, and A. Vincent, “Mi- crostructural development in equiatomic multicomponent alloys,” Materials Science and Engineering: A , vol. 375- 377, pp. 213–218, 2004. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S0921509303009936

2004
[267]

Phase formation prediction of high-entropy alloys: a deep learning study,

W. Zhu, W. Huo, S. Wang, X. Wang, K. Ren, S. Tan, F. Fang, Z. Xie, and J. Jiang, “Phase formation prediction of high-entropy alloys: a deep learning study,” journal of materials research and technology , vol. 18, pp. 800–809, 2022

2022
[268]

Element-wise representations with ecnet for material prop- erty prediction and applications in high-entropy alloys,

X. Wang, N.-D. Tran, S. Zeng, C. Hou, Y . Chen, and J. Ni, “Element-wise representations with ecnet for material prop- erty prediction and applications in high-entropy alloys,” npj Computational Materials, vol. 8, no. 1, p. 253, 2022

2022
[269]

Machine-learning microstructure for inverse material de- sign,

Z. Pei, K. A. Rozman, Ö. N. Do ˘gan, Y . Wen, N. Gao, E. A. Holm, J. A. Hawk, D. E. Alman, and M. C. Gao, “Machine-learning microstructure for inverse material de- sign,” Advanced Science, vol. 8, no. 23, p. 2101207, 2021

2021
[270]

Machine learning for high- entropy alloys: Progress, challenges and opportunities,

X. Liu, J. Zhang, and Z. Pei, “Machine learning for high- entropy alloys: Progress, challenges and opportunities,” Progress in Materials Science, vol. 131, p. 101018, 2023

2023
[271]

Much ado about nothing–a decade of porous materials research,

A. Thomas, “Much ado about nothing–a decade of porous materials research,” Nature Communications, vol. 11, no. 1, p. 4985, 2020

2020
[272]

Inverse design of porous materials: a diffusion model approach,

J. Park, A. P. S. Gill, S. M. Moosavi, and J. Kim, “Inverse design of porous materials: a diffusion model approach,” Journal of Materials Chemistry A, vol. 12, no. 11, pp. 6507– 6514, 2024

2024
[273]

Machine- learning phase prediction of high-entropy alloys,

W. Huang, P. Martin, and H. L. Zhuang, “Machine- learning phase prediction of high-entropy alloys,” Acta Materialia, vol. 169, pp. 225–236, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S1359645419301454

2019
[274]

A generative artificial intelligence framework based on a molecular diffu- sion model for the design of metal-organic frameworks for carbon capture,

H. Park, X. Yan, R. Zhu, E. A. Huerta, S. Chaudhuri, D. Cooper, I. Foster, and E. Tajkhorshid, “A generative artificial intelligence framework based on a molecular diffu- sion model for the design of metal-organic frameworks for carbon capture,” Communications Chemistry, vol. ...

2024
[275]

Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learn- ing,

H. Li, R. Yuan, H. Liang, W. Y . Wang, J. Li, and J. Wang, 39 “Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learn- ing,” Materials & Design, vol. 223, p. 111186, 2022

2022
[276]

Mofdiff: Coarse-grained diffusion for metal-organic framework design,

X. Fu, T. Xie, A. S. Rosen, T. Jaakkola, and J. Smith, “Mofdiff: Coarse-grained diffusion for metal-organic framework design,” arXiv preprint arXiv:2310.10732 , 2023

2023 arXiv
[277]

Multi-modal conditioning for metal-organic frameworks generation using 3d model- ing techniques,

J. Park, Y . Lee, and J. Kim, “Multi-modal conditioning for metal-organic frameworks generation using 3d model- ing techniques,” ChemRxiv, DOI: 10.26434/chemrxiv-2024- w8fps., 2024

2024 doi
[278]

Machine-learning-assisted material discovery of oxygen- rich highly porous carbon active materials for aqueous supercapacitors,

T. Wang, R. Pan, M. L. Martins, J. Cui, Z. Huang, B. P. Thapaliya, C.-L. Do-Thanh, M. Zhou, J. Fan, Z. Yanget al., “Machine-learning-assisted material discovery of oxygen- rich highly porous carbon active materials for aqueous supercapacitors,” Nature communications , vol. 14,...

2023
[279]

Function-led design of new porous materials,

A. G. Slater and A. I. Cooper, “Function-led design of new porous materials,” Science, vol. 348, no. 6238, p. aaa8075, 2015

2015
[280]

Leveraging machine learning in porous media,

M. Delpisheh, B. Ebrahimpour, A. Fattahi, M. Siavashi, H. Mir, H. Mashhadimoslem, M. A. Abdol, M. Ghorbani, J. Shokri, D. Niblett et al., “Leveraging machine learning in porous media,” Journal of Materials Chemistry A, 2024

2024
[281]

Machine learning in heteroge- neous porous materials,

M. D’Elia, H. Deng, C. Fraces, K. Garikipati, L. Graham- Brady, A. Howard, G. Karniadakis, V . Keshavarzzadeh, R. M. Kirby, N. Kutz et al., “Machine learning in heteroge- neous porous materials,” arXiv preprint arXiv:2202.04137, 2022

2022 arXiv
[282]

Inverse design of nanoporous crystalline reticular materials with deep generative models,

Z. Yao, B. Sánchez-Lengeling, N. S. Bobbitt, B. J. Bucior, S. G. H. Kumar, S. P. Collins, T. Burns, T. K. Woo, O. K. Farha, R. Q. Snurr et al. , “Inverse design of nanoporous crystalline reticular materials with deep generative models,” Nature Machine Intelligence, vol. 3, no....

2021
[283]

Induction of decision trees,

J. R. Quinlan, “Induction of decision trees,” Machine learn- ing, vol. 1, pp. 81–106, 1986

1986
[284]

Equivariant 3d-conditional diffusion model for molecular linker design,

I. Igashov, H. Stärk, C. Vignac, A. Schneuing, V . G. Sator- ras, P. Frossard, M. Welling, M. Bronstein, and B. Correia, “Equivariant 3d-conditional diffusion model for molecular linker design,” Nature Machine Intelligence , pp. 1–11, 2024

2024
[285]

Learn- ing representations by back-propagating errors,

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learn- ing representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986

1986
[286]

Deep learning,

I. Goodfellow, “Deep learning,” 2016

2016
[287]

Inverse design of mechanical metamate- rials with target nonlinear response via a neural accelerated evolution strategy,

B. Deng, A. Zareei, X. Ding, J. C. Weaver, C. H. Rycroft, and K. Bertoldi, “Inverse design of mechanical metamate- rials with target nonlinear response via a neural accelerated evolution strategy,” Advanced Materials, vol. 34, no. 41, p. 2206238, 2022

2022
[288]

Synthesis of covalent organic frameworks using sustainable solvents and machine learning,

S. Kumar, G. Ignacz, and G. Szekely, “Synthesis of covalent organic frameworks using sustainable solvents and machine learning,” Green Chemistry, vol. 23, no. 22, pp. 8932–8939, 2021

2021
[289]

Machine-learning-driven accelerated design-method for meta-devices,

S. Ijaz, S. Noureen, B. Rehman, O. Aldaghri, H. Cabr- era, K. H. Ibnaouf, N. Madkhali, and M. Q. Mehmood, “Machine-learning-driven accelerated design-method for meta-devices,” Materials Today Communications , vol. 37, p. 106951, 2023

2023
[290]

Applied machine learning as a driver for polymeric biomaterials design,

S. M. McDonald, E. K. Augustine, Q. Lanners, C. Rudin, L. Catherine Brinson, and M. L. Becker, “Applied machine learning as a driver for polymeric biomaterials design,” Nature Communications, vol. 14, no. 1, p. 4838, 2023

2023
[291]

Random forests,

L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5–32, 2001

2001
[293]

Support-vector networks,

C. Cortes, “Support-vector networks,” Machine Learning , 1995

1995
[297]

A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design,

X. Geng, Z. Cheng, S. Wang, C. Peng, A. Ullah, H. Wang, and G. Wu, “A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design,” Journal of Materials Science, vol. 57, no. 23, pp. 10 755–10 768, 2022

2022
[300]

Machine learning-driven real-time topology optimization under mov- ing morphable component-based framework,

X. Lei, C. Liu, Z. Du, W. Zhang, and X. Guo, “Machine learning-driven real-time topology optimization under mov- ing morphable component-based framework,” Journal of Applied Mechanics, vol. 86, no. 1, p. 011004, 2019

2019
[1994]

Available: https://link.aps.org/doi/10.1103/ PhysRevB.50.17953

[Online]. Available: https://link.aps.org/doi/10.1103/ PhysRevB.50.17953
[2017]

Available: https://link.aps.org/doi/10.1103/ RevModPhys.89.035002

[Online]. Available: https://link.aps.org/doi/10.1103/ RevModPhys.89.035002
[2021]

Available: https://link.aps.org/doi/10.1103/ PhysRevB.104.024401

[Online]. Available: https://link.aps.org/doi/10.1103/ PhysRevB.104.024401
[2022]

Available: https://www.mdpi.com/2079- 6412/12/8/1089

[Online]. Available: https://www.mdpi.com/2079- 6412/12/8/1089
[2023]

Available: https://doi.org/10.1038/s41524- 023-01131-3

[Online]. Available: https://doi.org/10.1038/s41524- 023-01131-3

doi
[2024]

Available: https://linkinghub.elsevier.com/ retrieve/pii/S254252932400035X

[Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S254252932400035X

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [73]

Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,

T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Phys. Rev. Lett. , vol. 120, p. 145301, Apr 2018. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.120.145301

doi:10.1103/physrevlett.120.145301 2018

[2] [1]

Inverse design in search of materials with target functionalities,

A. Zunger, “Inverse design in search of materials with target functionalities,” Nature Reviews Chemistry, vol. 2, no. 4, p. 0121, 2018

2018

[3] [2]

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,

J. Lee, D. Park, M. Lee, H. Lee, K. Park, I. Lee, and S. Ryu, “Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,” Materials Horizons, 2023

2023

[4] [3]

Inverse design of materials by machine learning,

J. Wang, Y . Wang, and Y . Chen, “Inverse design of materials by machine learning,” Materials, vol. 15, no. 5, p. 1811, 2022

2022

[5] [4]

Generative deep learning for the inverse design of materials

T. Long, Y . Zhang, and H. Zhang, “Generative deep learn- ing for the inverse design of materials,” arXiv preprint arXiv:2409.19124, 2024

work page Pith review arXiv 2024

[6] [5]

Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,

Y . Wu, “Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,” 2024

2024

[7] [6]

Generative models for inverse design of inorganic solid materials,

L. Chen, W. Zhang, Z. Nie, S. Li, and F. Pan, “Generative models for inverse design of inorganic solid materials,” J. Mater. Inform, vol. 1, no. 4, 2021

2021

[8] [7]

Inverse design of 3d cellular materials with physics-guided machine learning,

M. Abu-Mualla and J. Huang, “Inverse design of 3d cellular materials with physics-guided machine learning,”Materials & Design, vol. 232, p. 112103, 2023

2023