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REVIEW 6 major objections 6 minor 1 cited by

Artificial Intelligence and Generative Models for Materials Discovery -- A Review

T0 review · 6 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This review argues that generative models—by learning probability distributions over materials and sampling new structures from a latent space—represent a new paradigm of materials discovery, moving from explicit structure enumeration to…

desk verdict Useful survey with a clean taxonomy, but the applications section has enough citation-content mismatches that the review's orienting value is currently compromised. read the letter →

arxiv 2508.03278 v1 pith:VSBY4N64 submitted 2025-08-05 cond-mat.mtrl-sci cs.AIphysics.app-ph

Albertus Denny Handoko , Riko I Made This is my paper
classification cond-mat.mtrl-scics.AIphysics.app-ph
keywords generativemodelsmaterialsdiscoveryinversedesignlatentspacerepresentationmachinelearningvariationalautoencodersdiffusion
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

This review tries to establish that generative models in materials science represent a new discovery paradigm: instead of explicitly enumerating, substituting, or randomly placing atoms to find candidate structures, a model learns the probability distribution of valid materials and samples new structures from a latent space conditioned on desired properties. The authors survey six generative model families and five materials representations, and they use applications in batteries, catalysts, electronics, biomaterials, and high-throughput screening to argue that inverse design is becoming practical. If the review is right, the bottleneck in materials discovery shifts from structure search to data quality, synthesizability, and closed-loop experimental validation.

What carries the argument

The machinery that carries the argument is the latent-space generative model. Each reviewed model learns a probability distribution over materials representations—strings, graphs, voxel grids, or physics-informed descriptors—and generates candidates by sampling points in that learned space, optionally conditioned on properties through a predictor, conditional input, or reward function. The latent space is the bridge between structure and property that makes inverse design possible: desired properties select a region of the latent space, and decoding that region yields new structures that were not explicitly enumerated.

What would settle it

Check the primary sources behind the review's example applications: if the VAE paper credited with halide perovskites actually only generated drug-like molecules, or the antibiotic work credited to a GAN actually used a recurrent neural network, then the review's reliability as an orientation to the field would be directly falsified.

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Extended reading notes

Core claim

The central claim is the framing assertion that the ability of generative models to generate new structure suggestions from the latent space represents a new paradigm of materials discovery. The paper presents generative modeling as a fifth parent of AI-driven discovery, succeeding black-box optimization approaches that are difficult to generalize beyond their training tasks. By approximating the data distribution and sampling from a low-dimensional latent space, models can propose structures before experiments begin, conditioned on target properties. The review then catalogs variational autoencoders, generative adversarial networks, diffusion models, recurrent neural networks and transformers, normalizing flows, and generative flow networks, together with sequence, graph, voxel, physics-informed, and multimodal representations, and it surveys demonstrated applications and remaining challenges.

Load-bearing premise

The case for a new paradigm rests on the accuracy of the cited example applications; if the attributed VAE perovskite result or GAN antibiotic-coating result is not actually in the cited sources, the survey's map of the field would need correction.

Editorial extensions

If this is right

  • Discovery workflows can start from a target property and invert through the latent space to candidate materials, rather than screening known compounds.
  • Different model families have complementary failure modes—VAEs offer interpretable but blurry latent spaces, GANs produce sharp samples but can collapse, diffusion is stable but costly—so model choice should follow task constraints.
  • Representations decide what is learnable: SMILES-style strings are simple but lose three-dimensional geometry, while graphs and voxels capture structure at higher computational cost.
  • The remaining bottlenecks are data quality, scarcity, bias, interpretability, synthesizability, and computational cost, not model invention alone.
  • Closed-loop systems that feed experimental results back into generative models are the likely endpoint, reducing the distance between prediction and validated material.

Reading between the lines

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

  • A direct consequence the authors leave implicit is that the paradigm shift makes representation design and data curation as important as new model architectures, so progress may be measured by benchmark datasets that isolate representation from architecture.
  • If latent-space sampling truly outperforms explicit structure enumeration, an obvious test is a controlled comparison on a standardized open materials database: same compute, same target property, generative sampling versus random structure search followed by screening.
  • The authors' emphasis on closed-loop discovery suggests the biggest near-term gains will come from pairing generative models with automated synthesis and characterization, not from larger models alone.
  • Multimodal and physics-informed representations point to a future where generated candidates arrive with synthesis or characterization metadata attached, which would make inverse design directly actionable.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 6 minor

Summary. The paper is a review of AI-driven generative models for materials discovery. It surveys six model families (VAEs, GANs, diffusion models, RNNs/Transformers, normalizing flows, and GFlowNets), discusses five material representations (sequence, graph, voxel, physics-informed, and multimodal), and catalogs applications in energy storage, catalysis, electronics/photonics, biomaterials, and high-throughput screening. It also covers challenges such as data quality, interpretability, computational cost, and ethical considerations, and outlines emerging trends including foundation models, closed-loop experimental integration, and physics-informed architectures. The central framing claim is that sampling learned latent-space probability distributions represents a new paradigm of materials discovery, in contrast to explicit structure enumeration or substitution.

Significance. If accurate, the review would be a useful and timely consolidation of a fast-moving field. It assembles a broad reference base, reproduces standard equations for the main generative frameworks (ELBO, GAN minimax objective, diffusion noising, RNN recurrence, normalizing-flow change of variables, and GFlowNet flow-matching loss), and organizes the field into model families and representations with comparison tables and a roadmap figure. The paper also covers practical concerns (data bias, synthesizability, computational cost) that are relevant to experimentalists and computational researchers alike. However, the review's value as an orientation tool depends entirely on the reliability of its application catalog, and that catalog contains multiple verified attribution errors and internal contradictions. These errors are load-bearing because the review's contribution is the survey of examples, not a new derivation.

major comments (6)
  1. [Section 3.3] The semiconductor example is misattributed: the text states that Gómez-Bombarelli et al. (ref 46) "applied a VAE to generate sequence-based halide perovskites, trained on Materials Project band structure data, achieving 25% efficiency in tandem solar cells." Ref 46 is the ACS Central Science VAE paper for drug-like molecules; it contains no halide perovskites and no tandem solar cell efficiency. This is a fabricated example in a central application section and must be corrected or replaced with the actual source of any perovskite VAE result.
  2. [Sections 2.1.4 and 3.4] There is an internal contradiction about the Stokes et al. example. Section 2.1.4 states that Stokes et al. (ref 113) "used RNNs to generate novel antibiotics," while Section 3.4 states that "Stokes et al. 113 adapted GANs for antibiotic-inspired coatings." Ref 113 actually uses a directed message-passing neural network for antibiotic activity prediction, not an RNN or a GAN. This inconsistency directly affects Table 1, which classifies the model family for this flagship biomaterials application, and it illustrates that the model-to-application mapping in the review is unreliable.
  3. [Section 3.1 and Table 3] The solid-state electrolyte example is misattributed. Section 3.1 and Table 3 credit Vasylenko et al. (ref 122) with "a VAE to generate graph-based representations of garnet-type electrolytes" and a 15% higher conductivity validated by DFT. Ref 122 is an unsupervised machine-learning study on element selection for crystalline inorganic solids; it does not use a VAE and does not report a garnet electrolyte with 15% higher conductivity. This is a central energy-storage example, so the error is load-bearing for the review's credibility.
  4. [Section 3.2] The catalysis section misclassifies and misattributes the Honda et al. work. The text says "Honda et al. 53 using a SMILES Transformer, an RNN variant, to generate ligand sequences for homogeneous catalysts, trained on a ChEMBL dataset, reducing experimental iterations by 40% for olefin metathesis." Ref 53 is a drug-discovery paper introducing a pre-trained SMILES Transformer; a Transformer is not an RNN variant, and the paper does not address homogeneous catalysts or olefin metathesis. This is a concrete example of the attribution problems that pervade Section 3.
  5. [Section 3.1] A review article is incorrectly cited as a primary application. Section 3.1 states that ref 8 (Alverson et al., "Generative adversarial networks and diffusion models in material discovery") "employed a GAN to generate perovskite-based cathodes," with 10% higher capacity and experimental synthesis. Ref 8 is itself a review of GANs and diffusion models, not a primary study reporting perovskite cathodes. The same pattern appears in Section 3.4, where ref 127 (Winter et al., a paper on predicting limiting activity coefficients from SMILES) is credited with generating peptide sequences for tissue regeneration, and in Section 3.3, where ref 71 (the SELFIES-method paper) is credited with designing 2D materials using SELFIES and RNNs.
  6. [Sections 3.5 and 3.6] Further misattributions lower confidence in the catalog. Section 3.6 credits Zuo et al. (ref 141) with using "a VAE with Bayesian optimization" to prioritize shape-memory alloys, but ref 141 uses graph deep learning and Bayesian optimization, not a VAE. Section 3.5 cites Baird et al. (ref 10, the Xtal2png package) as demonstrating "AI-driven high-throughput library generation," which is not the content of that reference. These additional errors suggest that the attribution problems are not isolated typos but a systemic issue in the application sections.
minor comments (6)
  1. [Section 3.1] The sentence "7 extended diffusion models to porous carbon materials, optimizing pore structures for hydrogen uptake, validated via Monte Carlo simulations 61 and SymmCD 73 generate stable crystalline electrolytes..." is grammatically incomplete and conflates two different examples; ref 7 is a drug-design paper, not a porous-carbon study.
  2. [Section 2.2.1] The SMILES example "CCO(" described as "a representation for ethanol but missing a closing parenthesis" is confusing: the canonical SMILES for ethanol is "CCO" with no parentheses, so the intended illustration of an invalid string should be rewritten.
  3. [Section 2.1.4] The statement that Gómez-Bombarelli et al. (ref 46) "used LSTM-based VAEs" should be verified; the original paper uses a recurrent decoder (GRU-based) rather than an LSTM specifically.
  4. [Section 2.2.3] The claim that "MatterGen 137 likely employs voxel-like discretizations" is speculative and imprecise; MatterGen operates on atomic coordinates and lattice parameters in a diffusion framework, so the representation discussion should be corrected.
  5. [Sections 3.1 and 3.2] The sentence about Luo et al. (CrystalFlow) generating electrolytes with high ionic conductivity appears nearly verbatim in both Section 3.1 and Section 3.2; one occurrence should be removed.
  6. [References] The reference list contains malformed entries: [oec] is incomplete ("AI principles" with no authors or venue), and refs [2] and [3] lack author names and are only dated (2025).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the review summarizes external results and its self-citations are not load-bearing; attribution errors are accuracy concerns, not circular reasoning.

full rationale

No circular derivation chain is present. This is a review paper whose central claim, that generative models enable a new paradigm of materials discovery, is a framing statement rather than a result derived from equations or fitted data. The model equations quoted (VAE ELBO, GAN minimax objective, diffusion noise schedule, RNN update, normalizing-flow change of variables, GFlowNet flow-matching loss) are standard textbook identities reproduced from external sources, and no parameter is fitted to a subset of data and then renamed as a prediction. The authors' self-citations ([4], [49], [74], [80]) appear only in the introduction and in supporting examples about black-box optimization and process chemistry; they do not carry the review's central assertion about generative models. The concrete weaknesses identified by the reader, such as crediting Gomez-Bombarelli et al. with halide perovskite generation and describing Stokes et al. as using a GAN, are attribution and factual-accuracy errors that affect the review's trustworthiness as a survey, but they are not circularity because the claims do not reduce to their own inputs by construction. The review is self-contained as a survey of external literature, so the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

There are no fitted parameters or invented entities because the paper performs no modeling or data fitting. The two listed axioms are the background beliefs about generative modeling and material representations that the review's organization assumes.

assumptions (2)
  • domain assumption Generative models can learn the underlying probability distribution P(x) of material data and generate new valid samples by sampling from a latent space.
    Section 2 presents this as the core premise enabling inverse design. The review cites prior work but does not prove the premise itself.
  • domain assumption Standard material representations (SMILES, graphs, voxels, physics-informed, multimodal) sufficiently encode structure-property relationships for generative modeling.
    Section 2.2 builds the entire taxonomy on this premise, while also acknowledging that each representation has known deficiencies.

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Cite this review

Pith. "Pith review of Artificial Intelligence and Generative Models for Materials Discovery -- A Review." pith.science (2026). https://pith.science/paper/VSBY4N64

@misc{pith2026250803278,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence and Generative Models for Materials Discovery -- A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VSBY4N64}},
  note         = {Machine review of arXiv:2508.03278}
}
read the original abstract

High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial intelligence (AI) driven approach, realizing the 'inverse design' capabilities that allow the discovery of new materials given the desired properties. This review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery, including different materials representations available for this purpose. We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals while addressing challenges such as data scarcity, computational cost, interpretability, synthesizability, and dataset biases. Emerging approaches to overcome limitations and integrate AI with experimental workflows will be discussed, including multimodal models, physics informed architectures, and closed-loop discovery systems. This review aims to provide insights for researchers aiming to harness AI's transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation.

Figures

Figures reproduced from arXiv: 2508.03278 by the authors.

Figure 1
Figure 1. A paradigm shift in Materials Discovery. The strategy for discovering new materials has evolved over the last century. (a) Traditionally, materials discovery starts with an idea or hypothesis that needs to be validated through experimental synthesis, characterization, and testing. The advent of computational techniques allows ab-initio evaluation of the novel materials to gain deeper insights into the structure-prop… view at source ↗
Figure 2
Figure 2. Schematics of generative model architectures for materials discovery, illustrating the gen￾eral workflows for (a) VAE (Variational Autoencoder): encode-decode process with an inference and generative path, showcasing the mapping of molecules into a latent space distribution and sub￾sequent generation of new molecular structures. (b) GAN (Generative Adversarial Network): a generator creating molecular structures from… view at source ↗
Figure 3
Figure 3. An illustration of the use of VAE for molecular design, integrated with a joint property prediction model —predictor. The encoder transforms discrete molecular representations (like SMILES strings) into continuous latent space vectors. The decoder then converts these latent vectors back into SMILES strings. The predictor can be added to predict properties from latent representations. However, the huge size of the la… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: An illustration for MatGAN architecture consisting of a generator, which maps random vectors into generated samples, and a discriminator, which tries to differentiate real materials and generated ones. Taken with permission from Dan et al. 34 difficulties in capturing …
Figure 5
Figure 5. Figure 5: Stokes et al. 113 workflow for antibiotic discovery using deep learning (RNN) and chemical space exploration. RNN model, trained on 104 molecules (1), is validated (2) and subsequently used to predict antibiotic activity across a vast chemical space (up to 108 molecule…
Figure 6
Figure 6. Figure 6: Schematic illustration of four generative model representations for materials. (a) Sequence-based representation encodes material structures as linear strings (e.g., SMILES) for pro￾cessing by models like RNNs or Transformers. (b) Graph-based representation models atom…
Figure 7
Figure 7. Figure 7: An illustration of the MEGNet’s architecture, designed to predict properties of molecules and crystals (top panel). The central concept involves iterative message passing, where information is first exchanged and updated across Bonds (e′ k ), then aggregated at individ…
Figure 8
Figure 8. Figure 8: How multi-modal material representation is addressing the limitation of single represen￾tation. In this example, a Transformer model is used to integrate SMILES embeddings and graph embeddings of molecular structures for pre-training and downstream tasks. Input SMILES …
Figure 9
Figure 9. Figure 9: An illustration of zeolite generation via Diffusion model. (a) input structure representa￾tion, (b) the noising and denoising phases, and (c) the progressive sampling of zeolite grids. Taken with permission from94 3.1 Energy Storage and Battery Materials Generative mod…
Figure 10
Figure 10. Figure 10: An illustration of MOFformer’s architecture and self-supervised training. (a) MO￾Former processes Metal-Organic Frameworks (MOFs) by taking their unique MOFiD (e.g., qmof￾2521623) as input. This MOFiD is tokenised, embedded with positional encoding, and then fed throu…
Figure 11
Figure 11. Figure 11: Workflow of the AGoRaS-based VAE network. (a) chemical database information is compressed and decompressed to form a high-dimensional latent space. (b) Training and sampling of latent space to generate new compounds. Taken from 116 (CC-BY) for photonic devices. Yeung …
Figure 12
Figure 12. Figure 12: Roadmap of generative models in materials discovery, highlighting key milestones and projected future trends in model architectures, experimental integration, and solutions to challenges [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

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Forward citations

Cited by 1 Pith paper

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

  1. Chemical filters for ultra-high-throughput materials screening and generation

    cond-mat.mtrl-sci 2026-07 conditional novelty 6.0 of 10

    A configurable oxidation-state filter (SMACT-based) lets users tune strictness and doubles as an RL reward, improving chemical realism of generated crystals.

Reference graph

Works this paper leans on

140 extracted references · 75 canonical work pages · cited by 1 Pith paper

  1. [2]

    F AIR Principles

    (2025). F AIR Principles

    2025

  2. [3]

    Versailles Project on Advanced Materials and Standards (V AMAS)

    (2025). Versailles Project on Advanced Materials and Standards (V AMAS)

    2025

  3. [4]

    Agrotis, S., Emre Sener, M., Hagger, O. S. J., Handoko, A. D., and Caruana, D. J. (2024). One-step synthesis of nanosized cu-ag films using atmospheric pressure plasma jet. Applied Materials Today, 39:102286

    2024

  4. [5]

    L., Benabed, Y., Koziarski, M., and Schmidt, V

    AI4Science, M., Hernandez-Garcia, A., Duval, A., Volokhova, A., Bengio, Y., Sharma, D., Carrier, P. L., Benabed, Y., Koziarski, M., and Schmidt, V. (2023). Crystal-GFN: Sampling crystals with desirable proper- ties and constraints

    2023

  5. [6]

    H., Al-Sharify, T

    Al-Khaylani, H. H., Al-Sharify, T. A., Abbas, M. F., Hussein, H., Al-Shabandar, R., and Oleiwi, T. A. (2024). Generative Adversarial Networks to Design Metamaterials Based Nano-Photonics Devices. In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP) , pages 1–5

    2024

  6. [7]

    Alakhdar, A., Poczos, B., and Washburn, N. (2024). Diffusion Models in De Novo Drug Design. J. Chem. Inf. Model., 64(19):7238–7256

    2024

  7. [8]

    Baird, S., Murdock, R., Sin-Hang Ho, E., Johnson, J., and D

    Alverson, M., G. Baird, S., Murdock, R., Sin-Hang Ho, E., Johnson, J., and D. Sparks, T. (2024). Generative adversarial networks and diffusion models in material discovery. Digital Discovery, 3(1):62–80

    2024

  8. [9]

    Anstine, D. M. and Isayev, O. (2023). Generative Models as an Emerging Paradigm in the Chemical Sciences. J. Am. Chem. Soc. , 145(16):8736–8750

    2023

Show all 140 references
  1. [10]

    G., Jablonka, K

    Baird, S. G., Jablonka, K. M., Alverson, M. D., Sayeed, H. M., Khan, M. F., Seegmiller, C., Smit, B., and Sparks, T. D. (2022). Xtal2png: A Python package for representing crystalstructure as PNG files. JOSS, 7(76):4528

    2022

  2. [11]

    Bashir, N., Donti, P., Cuff, J., Sroka, S., Ilic, M., Sze, V., Delimitrou, C., and Olivetti, E. (2024). The climate and sustainability implications of generative ai. An MIT Exploration of Generative AI , page Online only

    2024

  3. [12]

    G., Wood, T

    Bedart, C., Shimokura, G., West, F. G., Wood, T. E., Batey, R. A., Irwin, J. J., and Schapira, M. (2024). The pan-canadian chemical library: A mechanism to open academic chemistry to high-throughput virtual screening. Scientific Data , 11(1):597. Bedart, Corentin Shimokura, Gr...

    2024 doi

  4. [13]

    J., Tiwari, M., and Bengio, E

    Bengio, Y., Lahlou, S., Deleu, T., Hu, E. J., Tiwari, M., and Bengio, E. (2023). GFlowNet Foundations. Journal of Machine Learning Research , 24(210):1–55

    2023

  5. [14]

    D., Piracha, H

    Breuck, P.-P. D., Piracha, H. A., Rignanese, G.-M., and Marques, M. A. L. (2025). A generative material transformer using Wyckoff representation

    2025

  6. [15]

    T., Choudhary, K., Csanyi, G., Ganose, A

    Butler, K. T., Choudhary, K., Csanyi, G., Ganose, A. M., Kalinin, S. V., and Morgan, D. (2024). Setting standards for data driven materials science. npj Computational Materials , 10(1):231

    2024

  7. [16]

    T., Davies, D

    Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., and Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715):547–555

    2018

  8. [17]

    Cao, N. D. and Kipf, T. (2022). MolGAN: An implicit generative model for small molecular graphs. 19

    2022

  9. [18]

    Cao, Z., Luo, X., Lv, J., and Wang, L. (2024). Space Group Informed Transformer for Crystalline Materials Generation

    2024

  10. [19]

    Cao, Z., Magar, R., Wang, Y., and Barati Farimani, A. (2023). MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction. J. Am. Chem. Soc. , 145(5):2958–2967

    2023

  11. [20]

    and Wang, L

    Cao, Z. and Wang, L. (2025). CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design

    2025

  12. [21]

    Chen, C., Ye, W., Zuo, Y., Zheng, C., and Ong, S. P. (2019). Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chem. Mater. , 31(9):3564–3572

    2019

  13. [22]

    Chen, J., Guo, J., and Schwaller, P. (2025a). MatInvent: Reinforcement Learning for 3D Crystal Diffusion Generation. In AI for Accelerated Materials Design - ICLR 2025

    2025

  14. [23]

    Chen, Y., Wang, X., Deng, X., Liu, Y., Chen, X., Zhang, Y., Wang, L., and Xiao, H. (2024). MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials

    2024

  15. [24]

    Chen, Z., Meng, Z., He, T., Li, H., Cao, J., Xu, L., Xiao, H., Zhang, Y., He, X., and Fang, G. (2025b). Crystal Structure Prediction Meets Artificial Intelligence. J. Phys. Chem. Lett. , 16(10):2581–2591

    2025

  16. [25]

    R., and Liu, H

    Cheng, L., Varshney, K. R., and Liu, H. (2021). Socially responsible ai algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research , 71:1137–1181

    2021

  17. [26]

    Chitre, A., Cheng, J., Ahmed, S., Querimit, R., Hippalgaonkar, K., and Lapkin, A. (2023). pHbot: Self- Driven Robot for pH Adjustment of Viscous Formulations via Physics-informed-ML. Preprint, Chemistry

    2023

  18. [27]

    R., Ramdas, A., Wu, Y., Rohr, B., Ermon, S., Dionne, J., Jornada, F

    Chitturi, S. R., Ramdas, A., Wu, Y., Rohr, B., Ermon, S., Dionne, J., Jornada, F. H. D., Dunne, M., Tassone, C., Neiswanger, W., and Ratner, D. (2024). Targeted materials discovery using bayesian algorithm execution. npj Computational Materials , 10(1):156

    2024

  19. [28]

    W., Eyke, N

    Coley, C. W., Eyke, N. S., and Jensen, K. F. (2020). Autonomous Discovery in the Chemical Sciences Part I: Progress. Angewandte Chemie International Edition , 59(51):22858–22893

    2020

  20. [29]

    R., Gordon, G

    Collins, C. R., Gordon, G. J., von Lilienfeld, O. A., and Yaron, D. J. (2018). Constant size descriptors for accurate machine learning models of molecular properties. The Journal of Chemical Physics , 148(24):241718

    2018

  21. [30]

    R., Stevanovic, V., Wadia, C., Guha, S., and Buonassisi, T

    Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V. R., Stevanovic, V., Wadia, C., Guha, S., and Buonassisi, T. (2018). Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule, 2(8):1410–1420

    2018

  22. [31]

    D., Simpson, T

    Cunningham, J. D., Simpson, T. W., and Tucker, C. S. (2019). An Investigation of Surrogate Models for Ef- ficient Performance-Based Decoding of 3D Point Clouds. Journal of Mechanical Design , 141(121401):121401

    2019

  23. [32]

    Curie, P., Curie, M., and B´ emont, G. (1898). Sur une nouvelle substance fortement radio-active, contenue dans la pechblende. Comptes rendus hebdomadaires des s´ eances de l’Acad´ emie des sciences, 127:1215–1217

  24. [33]

    L., Jahnatek, M., Chepulskii, R

    Curtarolo, S., Setyawan, W., Hart, G. L., Jahnatek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M. J., Stokes, H. T., Demchenko, D. O., and Morgan, D. (2012). Aflow: An automatic framework for high-throughput materials discovery. Computat...

    2012

  25. [34]

    Dan, Y., Zhao, Y., Li, X., Li, S., Hu, M., and Hu, J. (2020). Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput Mater , 6(1):1–7

    2020

  26. [35]

    Das, B., Peters, A., Li, G., and Hei, X. (2025). Generative Design of Thermoset Shape Memory Polymers Driven by Chemical Group: A Conditional Variational Autoencoder Approach. Journal of Polymer Science , 63(6):1334–1344

    2025

  27. [36]

    Das, K., Goyal, P., Lee, S.-C., Bhattacharjee, S., and Ganguly, N. (2023). Crysmmnet: Multimodal representation for crystal property prediction. In 39th Conference on Uncertainty in Artificial Intelligence , volume 216, pages 507–517. PMLR

    2023

  28. [37]

    M., Chin-Cheong, K., Palumbo, E., and Vogt, J

    Daunhawer, I., Sutter, T. M., Chin-Cheong, K., Palumbo, E., and Vogt, J. E. (2021). On the limitations of multimodal vaes. In The Tenth International Conference on Learning Representations

    2021

  29. [38]

    J., Jackson, N

    de Pablo, J. J., Jackson, N. E., Webb, M. A., Chen, L.-Q., Moore, J. E., Morgan, D., Jacobs, R., Pollock, T., Schlom, D. G., Toberer, E. S., Analytis, J., Dabo, I., DeLongchamp, D. M., Fiete, G. A., Grason, G. M., Hautier, G., Mo, Y., Rajan, K., Reed, E. J., Rodriguez, E., Ste...

    2019

  30. [39]

    Dobson, C. M. (2004). Chemical space and biology. Nature, 432(7019):824–828

    2004

  31. [40]

    Dong, R., Fu, N., Siriwardane, E. M. D., and Hu, J. (2024). Generative Design of Inorganic Compounds Using Deep Diffusion Language Models. J. Phys. Chem. A , 128(29):5980–5989

    2024

  32. [41]

    El-Awady, K. (2023). V AE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design

    2023

  33. [42]

    W., Volk, A

    Epps, R. W., Volk, A. A., Reyes, K. G., and Abolhasani, M. (2021). Accelerated AI development for autonomous materials synthesis in flow. Chem. Sci. , 12(17):6025–6036

    2021

  34. [43]

    Friederich, P., H¨ ase, F., Proppe, J., and Aspuru-Guzik, A. (2021). Machine-learned potentials for next- generation matter simulations. Nature Materials , 20(6):750–761. Friederich, Pascal Hase, Florian Proppe, Jonny Aspuru-Guzik, Alan eng Research Support, Non-U.S. Gov’t Rev...

    2021 doi

  35. [44]

    Fuhr, A. S. and Sumpter, B. G. (2022). Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation. Front. Mater., 9:865270

    2022

  36. [45]

    P., and Spadiut, O

    Gisperg, F., Klausser, R., Elshazly, M., Kopp, J., Brichtov´ a, E. P., and Spadiut, O. (2025). Bayesian optimization in bioprocess engineering—where do we stand today? Biotechnology and Bioengineering , 122(6):1313–1325. Gisperg, Florian Klausser, Robert Elshazly, Mohamed Kopp...

    2025 doi

  37. [46]

    N., Duvenaud, D., Hern´ andez-Lobato, J

    G´ omez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hern´ andez-Lobato, J. M., S´ anchez-Lengeling, B., She- berla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A. (2018). Automatic Chemical Design Using a Data-Driven Continuous Representation of...

    2018

  38. [47]

    J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y

    Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems , volume 27

    2014

  39. [48]

    Gui, J., Sun, Z., Wen, Y., Tao, D., and Ye, J. (2023). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering , 35(4):3313– 3332

    2023

  40. [49]

    K., Guo, Y., Chang, S

    Gupta, N. K., Guo, Y., Chang, S. Y., Lin, J., Khoo, Z. H. J., I Made, R., Ooi, Z.-E., Lim, C. Y. J., Lee, C. H., M, S., Lim, Y.-F., Khoo, E., Lu, W. F., Lum, Y., and Handoko, A. D. (2024). Towards a greener elec- trosynthesis: pairing machine learning and 3d printing for rapid...

    2024

  41. [50]

    Han, S., Kang, Y., Park, H., Yi, J., Park, G., and Kim, J. (2024). Multimodal transformer for property prediction in polymers. ACS Applied Materials & Interfaces , 16(13):16853–16860

    2024

  42. [51]

    Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., and Ceder, G. (2011). Data mined ionic substitutions for the discovery of new compounds. Inorganic Chemistry, 50(2):656–663

    2011

  43. [52]

    and Schmidhuber, J

    Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation , 9(8):1735– 1780

    1997

  44. [53]

    Honda, S., Shi, S., and Ueda, H. R. (2019). SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

    2019

  45. [54]

    Huang, Q., Li, Y., Zhu, L., Zhao, Q., and Yu, W. (2025). Unified multimodal multidomain polymer representation for property prediction. npj Computational Materials , 11(1):153

    2025

  46. [55]

    Y., Wang, H., and Ju, S

    Huang, X., Ma, S., Wu, Y., Wan, C., Zhao, C. Y., Wang, H., and Ju, S. (2023). High-throughput screening of amorphous polymers with high intrinsic thermal conductivity via automated physical feature engineering. Journal of Materials Chemistry A , 11(38):20539–20548

    2023

  47. [56]

    Ishikawa, A. (2022). Heterogeneous catalyst design by generative adversarial network and first-principles based microkinetics. Sci Rep, 12(1):11657

    2022

  48. [57]

    P., Hautier, G., Chen, W., Richards, W

    Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K. A. (2013a). Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Materials , 1(1):011002. 21

    2013

  49. [58]

    P., Hautier, G., Chen, W., Richards, W

    Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K. A. (2013b). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials , 1(1):011002

    2013

  50. [59]

    Jain, M., Deleu, T., Hartford, J., Liu, C.-H., Hernandez-Garcia, A., and Bengio, Y. (2023). GFlowNets for AI-driven scientific discovery. Digital Discovery, 2(3):557–577

    2023

  51. [60]

    Jiang, C., He, H., Guo, H., Zhang, X., Han, Q., Weng, Y., Fu, X., Zhu, Y., Yan, N., Tu, X., and Sun, Y. (2024). Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation. Nature Communications , 15(1):6301. Jiang, Chang He, Hongyuan Guo, Hon...

    2024

  52. [61]

    Jiao, R., Huang, W., Lin, P., Han, J., Chen, P., Lu, Y., and Liu, Y. (2023). Crystal Structure Prediction by Joint Equivariant Diffusion. Advances in Neural Information Processing Systems , 36:17464–17497

    2023

  53. [62]

    Karpovich, C., Pan, E., Jensen, Z., and Olivetti, E. (2023). Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction. Chem. Mater. , 35(3):1062–1079

    2023

  54. [63]

    Karpovich, C., Pan, E., and Olivetti, E. A. (2024). Deep reinforcement learning for inverse inorganic materials design. npj Computational Materials , 10(1):287

    2024

  55. [64]

    Kazeev, N., Nong, W., Romanov, I., Zhu, R., Ustyuzhanin, A., Yamazaki, S., and Hippalgaonkar, K. (2025). Wyckoff Transformer: Generation of Symmetric Crystals

    2025

  56. [65]

    E., Andersson, O

    Kelvinius, F. E., Andersson, O. B., Parackal, A. S., Qian, D., Armiento, R., and Lindsten, F. (2025). WyckoffDiff – A Generative Diffusion Model for Crystal Symmetry

    2025

  57. [66]

    Khastagir, S., Das, K., Goyal, P., Lee, S.-C., Bhattacharjee, S., and Ganguly, N. (2025). CrysLDM: Latent Diffusion Model for Crystal Material Generation. In AI for Accelerated Materials Design - ICLR 2025

    2025

  58. [67]

    Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B., Thiessen, P., Yu, B., Za- slavsky, L., Zhang, J., and Bolton, E. (2024). Pubchem 2025 update. Nucleic Acids Research, 53(D1):D1516– D1525

    2024

  59. [68]

    Kingma, D. P. and Welling, M. (2022). Auto-Encoding Variational Bayes

    2022

  60. [69]

    Klipfel, A., Fregier, Y., Sayede, A., and Bouraoui, Z. (2024). Vector Field Oriented Diffusion Model for Crystal Material Generation. Proceedings of the AAAI Conference on Artificial Intelligence , 38(20):22193– 22201

    2024

  61. [70]

    J., and Brubaker, M

    Kobyzev, I., Prince, S. J., and Brubaker, M. A. (2021). Normalizing Flows: An Introduction and Review of Current Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence , 43(11):3964–3979

    2021

  62. [71]

    Krenn, M., H¨ ase, F., Nigam, A., Friederich, P., and Aspuru-Guzik, A. (2020). Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation. Mach. Learn.: Sci. Technol., 1(4):045024

    2020

  63. [72]

    Lai, P., Amirkulova, F., and Gerstoft, P. (2021). Conditional Wasserstein generative adversarial networks applied to acoustic metamaterial design. The Journal of the Acoustical Society of America, 150(6):4362–4374

    2021

  64. [73]

    S., Kaba, S.-O., Zhu, Q., Lee, K

    Levy, D., Panigrahi, S. S., Kaba, S.-O., Zhu, Q., Lee, K. L. K., Galkin, M., Miret, S., and Ravanbakhsh, S. (2025). SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

    2025

  65. [74]

    Lim, C. Y. J., I Made, R., Khoo, Z. H. J., Ng, C. K., Bai, Y., Wang, J., Yang, G., Handoko, A. D., and Lim, Y.-F. (2023). Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting. Materials Horizons , 10(11):5022–5031. Lim, C...

    2023 doi

  66. [75]

    Lim, S., Lee, S., Piao, Y., Choi, M., Bang, D., Gu, J., and Kim, S. (2022). On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Computational and Structural Biotechnology Journal , 20:4288–4304. 22

    2022

  67. [76]

    Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., and Wang, J. (2018). Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat Commun , 9(1):3405

    2018

  68. [77]

    Luo, X., Wang, Z., Wang, Q., Lv, J., Wang, L., Wang, Y., and Ma, Y. (2025). CrystalFlow: A Flow-Based Generative Model for Crystalline Materials

    2025

  69. [78]

    P., Parlane, F

    MacLeod, B. P., Parlane, F. G. L., Brown, A. K., Hein, J. E., and Berlinguette, C. P. (2021). Flexible automation accelerates materials discovery. Nat. Mater. , pages 1–5

    2021

  70. [79]

    P., Parlane, F

    MacLeod, B. P., Parlane, F. G. L., Rupnow, C. C., Dettelbach, K. E., Elliott, M. S., Morrissey, T. D., Haley, T. H., Proskurin, O., Rooney, M. B., Taherimakhsousi, N., Dvorak, D. J., Chiu, H. N., Waizenegger, C. E. B., Ocean, K., Mokhtari, M., and Berlinguette, C. P. (2022). A...

    2022

  71. [80]

    H., I Made, R., Parkin, I

    Mehta, K. H., I Made, R., Parkin, I. P., Sankar, G., and Handoko, A. D. (2025). A paradigm shift: From batch processing to flow chemistry. Small, page e2411519. Mehta, Kallum Hiten I Made, Riko Parkin, Ivan P Sankar, Gopinathan Handoko, Albertus Denny eng C231218004/A*STAR Hor...

    2025

  72. [81]

    S., Aykol, M., Cheon, G., and Cubuk, E

    Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., and Cubuk, E. D. (2023). Scaling deep learning for materials discovery. Nature, 624(7990):80–85

    2023

  73. [82]

    K., Chen, R

    Miller, B. K., Chen, R. T. Q., Sriram, A., and Wood, B. M. (2024). FlowMM: Generating Materials with Riemannian Flow Matching. In Forty-First International Conference on Machine Learning

    2024

  74. [83]

    M., Srikumar, V., and Sparks, T

    Mohanty, T., Mehta, M., Sayeed, H. M., Srikumar, V., and Sparks, T. D. (2024). CrysText: A Generative AI Approach for Text-Conditioned Crystal Structure Generation using LLM

    2024

  75. [84]

    and Tieleman, D

    Monticelli, L. and Tieleman, D. P. (2013). Force Fields for Classical Molecular Dynamics , pages 197–213. Humana Press, Totowa, NJ

    2013

  76. [85]

    Mullin, R. (2023). The ethics of ai in the lab. C&EN Global Enterprise , 101(31):30–35

    2023

  77. [86]

    P., Ortner, C., Cs´ anyi, G., and Ceriotti, M

    Musil, F., Grisafi, A., Bart´ ok, A. P., Ortner, C., Cs´ anyi, G., and Ceriotti, M. (2021). Physics-Inspired Structural Representations for Molecules and Materials. Chem. Rev., 121(16):9759–9815

    2021

  78. [87]

    S., Sanchez-Lengeling, B., Gregoire, J

    Noh, J., Kim, J., Stein, H. S., Sanchez-Lengeling, B., Gregoire, J. M., Aspuru-Guzik, A., and Jung, Y. (2019). Inverse Design of Solid-State Materials via a Continuous Representation. Matter, 1(5):1370–1384

    2019

  79. [88]

    Nong, W., Zhu, R., and Hippalgaonkar, K. (2024). CrySPR: A Python interface for implementation of crystal structure pre-relaxation and prediction using machine-learning interatomic potentials

    2024

  80. [89]

    Nouira, A., Sokolovska, N., and Crivello, J.-C. (2019). CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

    2019

  81. [90]

    Orlando, G., Raimondi, D., Duran-Roma˜ na, R., Moreau, Y., Schymkowitz, J., and Rousseau, F. (2022). Pyuul provides an interface between biological structures and deep learning algorithms. Nature Communica- tions, 13(1):961

    2022

  82. [91]

    d., D´ ecatoire, R., and Mougeot, M

    Oubari, F., Mathelin, A. d., D´ ecatoire, R., and Mougeot, M. (2021). A binded vae for inorganic material generation. In NeurIPS 2021 · Thirty-Fifth Annual Conference on Neural Information Processing Systems , Workshop on Deep Generative Models and Downstream Applications

    2021

  83. [92]

    Pan, E., Kwon, S., Liu, S., Xie, M., Duan, Y., Prein, T., Sheriff, K., Roman, Y., Moliner, M., Gomez- Bombarelli, R., and Olivetti, E. (2024). A Chemically-Guided Generative Diffusion Model for Materials Synthesis Planning. In AI for Accelerated Materials Design - NeurIPS 2024

    2024

  84. [93]

    J., Mohamed, S., and Lakshminarayanan, B

    Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2021). Nor- malizing Flows for Probabilistic Modeling and Inference. Journal of Machine Learning Research, 22(57):1–64

    2021

  85. [94]

    P., Mohamad Moosavi, S., and Kim, J

    Park, J., Singh Gill, A. P., Mohamad Moosavi, S., and Kim, J. (2024). Inverse design of porous materials: A diffusion model approach. Journal of Materials Chemistry A , 12(11):6507–6514

    2024

  86. [95]

    Pickard, C. J. and Needs, R. J. (2011). Ab initio random structure searching.Journal of Physics: Condensed Matter, 23(5):053201. 23

    2011

  87. [96]

    J., Wolverton, C., Hattrick-Simpers, J., and Mehta, A

    Ren, F., Ward, L., Williams, T., Laws, K. J., Wolverton, C., Hattrick-Simpers, J., and Mehta, A. (2018). Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experi- ments. Science Advances, 4(4):eaaq1566

    2018

  88. [97]

    Reynard, K., Barry, T., and Reynard, K. (1992). Standards for Materials Databases - National and International Programmes - Do They Provide for Data Users Needs? , volume STP1140-EB, page 0. ASTM International

    1992

  89. [98]

    and Zavadlav, J

    R¨ ocken, S. and Zavadlav, J. (2024). Accurate machine learning force fields via experimental and simulation data fusion. npj Comput Mater , 10(1):69

    2024

  90. [99]

    Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386–408. ROSENBLATT, F eng 1958/11/01 Psychol Rev. 1958 Nov;65(6):386-408. doi: 10.1037/h0042519

    1958 doi

  91. [100]

    Rummukainen, H., H¨ orhammer, H., Kuusela, P., Kilpi, J., Sirvi¨ o, J., and M¨ akel¨ a, M. (2024). Traditional or adaptive design of experiments? a pilot-scale comparison on wood delignification. Heliyon, 10(2):e24484

    2024

  92. [101]

    and Haber, E

    Ruthotto, L. and Haber, E. (2021). An introduction to deep generative modeling. GAMM-Mitteilungen, 44(2):e202100008

    2021

  93. [102]

    E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C

    Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C. (2013). Materials design and discovery with high-throughput density functional theory: The open quantum materials database (oqmd). JOM, 65(11):1501–1509

    2013

  94. [103]

    and Aspuru-Guzik, A

    Sanchez-Lengeling, B. and Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361(6400):360–365

    2018

  95. [104]

    and Fechner, U

    Schneider, G. and Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov, 4(8):649–663

    2005

  96. [105]

    Schultz, P. G. and Lerner, R. A. (1995). From molecular diversity to catalysis: Lessons from the immune system. Science, 269(5232):1835–1842. Schultz, P G Lerner, R A eng Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. Research Support, U.S. Gov’t, P....

    1995 doi

  97. [106]

    Schultz, P. G. and Xiang, X.-D. (1998). Combinatorial approaches to materials science. Current Opinion in Solid State and Materials Science , 3(2):153–158

    1998

  98. [107]

    E., Chmiela, S., Tkatchenko, A., and M¨ uller, K.-R

    Sch¨ utt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and M¨ uller, K.-R. (2017). SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. In Advances in Neural Information Processing Systems , volume 30

    2017

  99. [108]

    Segler, M. H. S., Kogej, T., Tyrchan, C., and Waller, M. P. (2018). Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci. , 4(1):120–131

    2018

  100. [109]

    and Komodakis, N

    Simonovsky, M. and Komodakis, N. (2018). GraphV AE: Towards Generation of Small Graphs Using Variational Autoencoders. In Artificial Neural Networks and Machine Learning – ICANN 2018 , pages 412– 422

    2018

  101. [110]

    Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, pages 2256–2265

    2015

  102. [111]

    Sohn, K., Lee, H., and Yan, X. (2015). Learning Structured Output Representation using Deep Conditional Generative Models. In Advances in Neural Information Processing Systems , volume 28

    2015

  103. [112]

    K., Chen, R

    Sriram, A., Miller, B. K., Chen, R. T., and Wood, B. M. (2024). FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions. Advances in Neural Information Processing Systems, 37:46025–46046

    2024

  104. [113]

    M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N

    Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzil...

    2020

  105. [114]

    Sun, S., Wang, X., Jiang, Y., Lei, Y., Zhang, S., Kumar, S., Zhang, J., Ma, E., Mazzarello, R., Wang, J.-J., and Zhang, W. (2024). High-throughput screening to identify two-dimensional layered phase-change chalcogenides for embedded memory applications. npj Computational Mater...

    2024

  106. [115]

    Team, T. F. (2017). A principled ai discussion in asilomar

    2017

  107. [116]

    and Musho, T

    Tempke, R. and Musho, T. (2022). Autonomous design of new chemical reactions using a variational autoencoder. Commun Chem , 5(1):1–10

    2022

  108. [117]

    Tonegawa, S. (1983). Somatic generation of antibody diversity. Nature, 302(5909):575–581

    1983

  109. [118]

    P., Curry, J

    Trask, N., Martinez, C., Shilt, T., Walker, E., Lee, K., Garland, A., Adams, D. P., Curry, J. F., Dugger, M. T., Larson, S. R., and Boyce, B. L. (2024). Unsupervised physics-informed disentanglement of multimodal materials data. Materials Today, 80:286–296

    2024

  110. [119]

    T., Chmiela, S., Sauceda, H

    Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Sch¨ utt, K. T., Tkatchenko, A., and M¨ uller, K.-R. (2021). Machine learning force fields. Chemical Reviews, 121(16):10142–10186. Unke, Oliver T Chmiela, Stefan Sauceda, Huziel E Gastegger, Michael Poltav...

    2021 doi

  111. [120]

    K., Choudhary, K., Mehta, A., Smith, R., Kusne, G., Tavazza, F., Vlcek, L., Ziatdinov, M., Kalinin, S

    Vasudevan, R. K., Choudhary, K., Mehta, A., Smith, R., Kusne, G., Tavazza, F., Vlcek, L., Ziatdinov, M., Kalinin, S. V., and Hattrick-Simpers, J. (2019). Materials science in the artificial intelligence age: High-throughput library generation, machine learning, and a pathway f...

    2019

  112. [121]

    N., ukasz Kaiser, L., and Polosukhin, I

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ukasz Kaiser, L., and Polosukhin, I. (2017). Attention is All you Need. In Advances in Neural Information Processing Systems , volume 30

    2017

  113. [122]

    B., Gusev, V

    Vasylenko, A., Gamon, J., Duff, B. B., Gusev, V. V., Daniels, L. M., Zanella, M., Shin, J. F., Sharp, P. M., Morscher, A., Chen, R., Neale, A. R., Hardwick, L. J., Claridge, J. B., Blanc, F., Gaultois, M. W., Dyer, M. S., and Rosseinsky, M. J. (2021). Element selection for cry...

    2021

  114. [123]

    S., Hyatt, J

    Wang, J. S., Hyatt, J. S., and Fish, M. (2024). Using conditional normalizing flows to generate material placements in an optimized thermal composite. International Journal of Heat and Mass Transfer, 224:125287

    2024

  115. [124]

    Wang, R., Xu, C., Dong, R., Luo, Z., Zheng, R., and Zhang, X. (2023a). A secured big-data sharing platform for materials genome engineering: State-of-the-art, challenges and architecture. Future Generation Computer Systems , 142:59–74

    2023

  116. [125]

    Wang, Y., Chen, S., Chen, G., Shurberg, E., Liu, H., and Hong, P. (2023b). Motif-based graph represen- tation learning with application to chemical molecules. Informatics, 10(1):8

    2023

  117. [126]

    Weininger, D. (1988). SMILES, a chemical language and information system. 1. Introduction to method- ology and encoding rules. J. Chem. Inf. Comput. Sci. , 28(1):31–36

    1988

  118. [127]

    Winter, B., Winter, C., Schilling, J., and Bardow, A. (2022). A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing. Digital Discovery, 1(6):859–869

    2022

  119. [128]

    Xie, T., Fu, X., Ganea, O.-E., Barzilay, R., and Jaakkola, T. (2022). Crystal Diffusion Variational Autoencoder for Periodic Material Generation

    2022

  120. [129]

    and Grossman, J

    Xie, T. and Grossman, J. C. (2018). Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. , 120(14):145301

    2018

  121. [130]

    Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., and Yang, M.-H. (2023). Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Comput. Surv. , 56(4):105:1– 105:39

    2023

  122. [131]

    and Perdikaris, P

    Yang, Y. and Perdikaris, P. (2018). Physics-informed deep generative models. In3rd workshop on Bayesian Deep Learning (NeurIPS)

    2018

  123. [132]

    W., and Raman, A

    Yeung, C., Tsai, R., Pham, B., King, B., Kawagoe, Y., Ho, D., Liang, J., Knight, M. W., and Raman, A. P. (2021). Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning. Advanced Optical Materials, 9(20):2100548. 25

    2021

  124. [133]

    Yong, A. X. B., Su, T., and Ertekin, E. (2024). Dismai-Bench: Benchmarking and designing generative models using disordered materials and interfaces. Digital Discovery, 3(9):1889–1909

    2024

  125. [134]

    Zagorac, D., M¨ uller, H., Ruehl, S., Zagorac, J., and Rehme, S. (2019). Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features. Journal of Applied Crystallography, 52(5):918–925. Zagorac, D Muller, H Ruehl, S Z...

    2019 doi

  126. [135]

    V., Tanvir, A

    Zeng, M., Du, Y., Jiang, Q., Kempf, N., Wei, C., Bimrose, M. V., Tanvir, A. N. M., Xu, H., Chen, J., Kirsch, D. J., Martin, J., Wyatt, B. C., Hayashi, T., Saeidi-Javash, M., Sakaue, H., Anasori, B., Jin, L., Mcmurtrey, M. D., and Zhang, Y. (2023). High-throughput printing of c...

    2023

  127. [136]

    Zeni, C., Pinsler, R., Z¨ ugner, D., Fowler, A., Horton, M., Fu, X., Shysheya, S., Crabb´ e, J., Sun, L., Smith, J., Nguyen, B., Schulz, H., Lewis, S., Huang, C.-W., Lu, Z., Zhou, Y., Yang, H., Hao, H., Li, J., Tomioka, R., and Xie, T. (2024). MatterGen: A generative model for...

    2024

  128. [137]

    Zeni, C., Pinsler, R., Z¨ ugner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabb´ e, J., Ueda, S., Sordillo, R., Sun, L., Smith, J., Nguyen, B., Schulz, H., Lewis, S., Huang, C.-W., Lu, Z., Zhou, Y., Yang, H., Hao, H., Li, J., Yang, C., Li, W., Tomioka, R., a...

    2025

  129. [138]

    Zhang, H., Fu, H., Zhu, S., Yong, W., and Xie, J. (2021). Machine learning assisted composition effective design for precipitation strengthened copper alloys. Acta Materialia, 215:117118

    2021

  130. [139]

    Zhu, Q., Liu, Z., and Yan, J. (2020). Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks

    2020

  131. [140]

    Zhu, R., Nong, W., Yamazaki, S., and Hippalgaonkar, K. (2024). WyCryst: Wyckoff inorganic crystal generator framework. Matter, 7(10):3469–3488

    2024

  132. [141]

    Zuo, Y., Qin, M., Chen, C., Ye, W., Li, X., Luo, J., and Ong, S. P. (2021). Accelerating materials discovery with Bayesian optimization and graph deep learning. Materials Today, 51:126–135. 26

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.