A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

Chen Zhao; Minh-Hao Van; Prateek Verma; Xintao Wu

REVIEW 4 major objections 4 minor 3 cited by

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read This survey argues that foundation models and LLM agents are moving materials science from task-specific machine learning to general-purpose, transferable AI, and maps the field into six task areas.

desk verdict A broad, well-organized survey whose central claim of comprehensive multiscale coverage is undercut by an internal contradiction about Marcato FM; useful as a map, but needs a revision before it can be trusted as a reference. read the letter →

arxiv 2506.20743 v1 pith:2IQCX44V submitted 2025-06-25 cs.LG cs.CE

Minh-Hao Van , Prateek Verma , Chen Zhao , Xintao Wu This is my paper

classification cs.LGcs.CE

keywords foundationmodelsmaterialssciencelargelanguageLLMagentsdatasetstaxonomyatomisticsimulationpropertyprediction

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 survey tries to establish that foundation models, meaning large pretrained models that transfer across tasks and data types, have begun to reshape materials science and that the field can be usefully mapped by what these models do. It organizes current work into six task areas: data extraction and question answering, atomistic simulation, property prediction, materials design and discovery, process planning and optimization, and multiscale modeling. It further classifies the models themselves as unimodal, multimodal, or LLM-based agents, and pairs them with the datasets, benchmarks, and toolkits that support them. The early successes it documents, such as GNoME's 2.2 million predicted stable materials, MatterSim's zero-shot force fields, and A-Lab's 71 percent autonomous synthesis success rate, are offered as evidence that the shift is already producing results. The paper's intended contribution is a usable map and roadmap for researchers choosing models, data, and tools.

What carries the argument

The load-bearing machinery is the survey's six-category task taxonomy (T1–T6), together with its three-way model classification: unimodal foundation models, multimodal foundation models, and LLM agents. The taxonomy is what converts a scattered set of papers, datasets, and tools into a map: each model is assigned to the tasks it can perform, and each dataset or benchmark is tied to the tasks it serves. The Transformer architecture supplies the common engine underneath most of the surveyed models, and the taxonomy is what lets the authors make comparative statements about coverage, gaps, and future directions.

What would settle it

A reader could re-run a systematic search with documented queries and inclusion criteria over the same period and check whether significant foundation models, datasets, or tools are missing from the six categories, or whether any quoted success figure, such as GNoME's 2.2 million materials or A-Lab's 71 percent rate, is misattributed; a major omission or misattribution would refute the survey's comprehensiveness claim.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that foundation models are catalyzing a transformative shift in materials science by replacing narrow, task-specific machine-learning pipelines with general-purpose systems that transfer across modalities and tasks. The survey's contribution is a task-driven taxonomy of six application areas (data extraction, interpretation and Q&A; atomistic simulation; property prediction; materials structure, design and discovery; process planning, discovery and optimization; and multiscale modeling), a division of the models into unimodal, multimodal, and LLM-agent categories, and a curated inventory of datasets, benchmarks, tools, and autonomous experimental platforms. Read sympathetically, the paper is saying that the pieces for generalist AI-driven materials research now exist and are converging, while acknowledging that long-range interactions, data imbalance, interpretability, and experimental integration remain unsolved.

Load-bearing premise

The survey's map is only as reliable as its unstated choice of which papers and tools to include, since the authors say they selectively incorporated high-quality research from Google Scholar and relied on prior experience for tools, without reporting search queries, inclusion criteria, or a verification protocol.

Editorial extensions

If this is right

If the taxonomy is accepted, researchers can locate a model, dataset, or tool for a given task by consulting the corresponding category, which lowers the barrier to adopting AI in materials labs.
The documented early successes imply that universal machine-learned potentials and generative models can operate at scales that conventional DFT screening cannot, at least for crystalline inorganic materials.
The survey's limitations discussion implies that progress on polymers, disordered solids, and multiscale modeling depends on new kinds of data rather than on new architectures alone.
The appearance of benchmarks such as LLM4Mat-Bench and MACBENCH suggests that evaluating LLMs and multimodal models on materials tasks is becoming standard practice.
If agentic systems like MatPilot mature, experimental workflows could shift toward closed-loop, human-in-the-loop discovery, with LLMs planning and robots executing.

Reading between the lines

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

My inference: the same six-task taxonomy could serve as a checklist for evaluating any new 'materials foundation model' claim, since a model that cannot be placed in at least one category is probably not yet a foundation model for this field.
I infer that the lack of reported search queries and inclusion criteria means the survey should be read as a curated snapshot rather than an exhaustive census, and that quantitative statements like GNoME's 2.2 million materials are inherited from the cited papers rather than independently verified here.
I infer that the next test of the field will be whether multimodal, co-registered datasets appear that pair structure with synthesis history and spectra; the survey identifies their absence as a bottleneck, and their arrival would likely accelerate the agentic systems it describes.

Share X Bluesky LinkedIn Reddit HN

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

What you should know first: this is a useful survey, not a research claim. The six-task taxonomy (T1–T6) is a reasonable organizing device, and the survey stacks up a large amount of current work across models, datasets, and tools. Newcomers will get a genuinely serviceable map. The coverage of unimodal, multimodal, and LLM-agent families is broad and mostly accurate on the papers I checked.

The most valuable part is the joint treatment of models, datasets, and infrastructures; few prior surveys bring those together. Tables 1–4 are dense but usable. I also think the authors are honest about limitations (long-range interactions, data imbalance, interpretability) in Section 5.2.

But there is a real internal contradiction, and it sits close to the paper's center. Section 3.1.6 states flatly that "there are no existing foundation model architectures specifically designed for multiscale modeling problems in materials science." Yet Table 1 lists Marcato FM with task T6, and Section 3.2 describes it as a cross-modal encoder-decoder for material failure prediction combining simulation outputs and grid-based fields. The paper can't have both. Either T6 is empty (contradicting why the survey claims to cover multiscale modeling as a distinguishing category) or Marcato FM is a T6 foundation model (contradicting 3.1.6). A reader relying on Table 1 would overestimate the maturity of the category; a reader relying on 3.1.6 would be told the opposite. This needs a fix: drop the blanket statement, or remove/reclassify the T6 entry, and add a sentence explaining why Marcato FM doesn't count if it doesn't.

The second soft spot is the literature selection. Section 1 says the authors "selectively incorporated high-quality research" and chose tools from "prior experience," but give no search queries, inclusion criteria, or verification protocol. For a survey whose abstract promises "comprehensive overview," that omission matters. A clear scope statement and a documented search date would make the taxonomy auditable.

Everything else is proportional. No derivations to check; the factual summaries track their cited sources. I didn't find fabrication or invented entities. The stress-test note about the multiscale contradiction holds up on reading — it's not a misfire.

Who gets value: grad students and researchers entering AI-for-materials; people writing related-work sections. It deserves a serious referee — a revision can fix the contradiction and scope documentation, and the result would be a solid reference. Send it to review.

Referee Report

4 major / 4 minor

Summary. This survey reviews foundation models (FMs), large language model (LLM) agents, datasets, and computational tools in materials science. It proposes a six-task taxonomy (T1–T6: data extraction/Q&A, atomistic simulation, property prediction, structure design/discovery, process planning/optimization, and multiscale modeling) and organizes the model landscape into unimodal FMs, multimodal FMs, and LLM agents. Tables summarize representative models, datasets, and tools, and the text discusses early successes (e.g., GNoME, MatterSim, A-Lab), limitations, and future directions. The central claimed contribution is a comprehensive, task-driven map of the field that goes beyond prior surveys by including atomistic simulation and multiscale modeling.

Significance. If accurate, this survey would be a useful reference for researchers entering the area, and the task-driven taxonomy is a sensible organizing principle. The tables are dense and potentially valuable. The paper does not make empirical claims of its own, so its correctness rests entirely on faithful reporting of cited work and on the representativeness of its selection. That dependence makes the internal inconsistencies and factual errors identified below consequential: they directly affect the survey's reliability as a map of the field. The coverage of tools and datasets is broad, and the discussion of limitations is balanced, but the undocumented selection protocol and the T6 contradiction must be resolved before the 'comprehensive' claim can be accepted.

major comments (4)

[§3.1.6, Table 1, §3.2] Section 3.1.6 states, 'there are no existing foundation model architectures specifically designed for multiscale modeling problems in materials science.' This directly contradicts Table 1, which lists Marcato FM as a multimodal foundation model with task T6, and Section 3.2, which describes Marcato FM as a cross-modal encoder-decoder model for material failure prediction combining simulation outputs, structure, and grid-based fields. Since the survey uses the inclusion of multiscale modeling (T6) as one of its differentiators from prior work in Section 1, this is a load-bearing contradiction: either Marcato FM qualifies as a T6 foundation model and the Section 3.1.6 statement is false, or it does not and the table and Section 3.2 misclassify it. The authors must reconcile these statements, ideally by defining more precisely what counts as a 'foundation model' for T6 and then applying that definition consistently.
[Abstract and §5.1] The abstract and Section 5.1 state that GNoME 'discovered over 2.2 million new stable materials' (or '2.2M new stable inorganic materials'). The cited source (Merchant et al., Nature 624, 2023) reports 2.2 million new crystals, of which roughly 380,000 are predicted to be stable. The survey's phrasing inflates the number of stable materials by about a factor of six, overstating one of the field's headline successes. This should be corrected in both the abstract and Section 5.1 to match the cited numbers.
[§1] The paper claims to provide 'a comprehensive overview' and to have conducted 'a systematic literature search using Google Scholar,' but it does not report the search queries, date range, inclusion/exclusion criteria, or the number of papers screened versus included. The statement that the authors 'selectively incorporated high-quality research published in top-tier venues, as well as influential preprints' and chose tools 'based on our prior experience' is not a reproducible protocol. Given that the survey's value depends on the representativeness of its selection, the authors should add a methodology subsection (or appendix) specifying search strings, screening steps, and a clear selection policy so readers can assess coverage and potential omissions.
[Table 1 and §3.1] Table 1, labeled 'Representative unimodal, multimodal, and agent-based foundation models,' includes models that do not fit the paper's own definition of foundation models as 'large, pretrained models trained on broad, diverse datasets' (Section 1 and Section 3). For example, CGCNN, MEGNet, CDVAE, and DiffCSP are task-specific architectures trained for particular prediction or generation tasks, not large pretrained general-purpose models. Including them under the 'Unimodal Foundation Models' heading conflates general-purpose pretrained models with conventional deep learning models, which misrepresents the maturity of the foundation-model landscape. The authors should either restrict the table to genuine foundation models or add a clear distinction (e.g., a separate category for 'task-specific deep learning models') so the taxonomy is not misleading.

minor comments (4)

[§3 (first paragraph)] There is a duplicated word in the sentence 'The input is tokenized from the model's vocabulary and and each token is represented as a vector.'
[Figure 3] In the timeline figure, 'ALIGN-FF' should be 'ALIGNN-FF' and 'FORCE' should be 'FORGE'; also 'Nach0' appears with inconsistent capitalization relative to the text's 'nach0'.
[Table 2] The OQMD row lists 'T6' (multiscale modeling) among its relevant tasks, but OQMD is an atomistic DFT database; T6 (multiscale) appears to be an error. The same may apply to the Materials Project row, where T6 is listed without evident support.
[§4.2.2] The text mentions 'Geom3D' and 'JARVIS-Leaderboard' as platforms but provides no citations for them. Please add appropriate references or remove these mentions if they are not accessible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the survey makes no derived predictions and its taxonomy is an organizational scheme, not a fitted or self-referential result.

full rationale

This manuscript is a literature survey rather than a derivation or empirical study. It introduces a six-category task taxonomy (T1–T6) as an organizing framework and summarizes models, datasets, and tools from external work. There is no fitted parameter, no predicted quantity that is statistically forced by an input subset, and no derivation chain in which an output is equivalent to an input by construction. The paper's claims about model performance, such as GNoME's 2.2M materials and A-Lab's 71% success rate, are reported from cited external sources, not produced by the survey itself. The authors do not invoke a load-bearing uniqueness theorem from their own prior work, and the taxonomy is not defined circularly in terms of the models it categorizes. The only notable inconsistency is that Section 3.1.6 states there are no existing foundation model architectures specifically designed for multiscale modeling, while Table 1 and Section 3.2 list Marcato FM with task T6; however, this is an internal consistency and correctness issue, not circularity, because neither statement is derived from the other or from the survey's own fitted inputs. Accordingly, the circularity score is 0.

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

No free parameters or invented entities appear. The survey's reliance is entirely on literature coverage and taxonomy choices.

assumptions (3)

domain assumption Six-task taxonomy (T1-T6) is a meaningful, comprehensive partition of AI-for-materials work.
The entire organization of the survey rests on this split (Section 1, Figure 1); it is a classification choice, not a measured fact.
domain assumption Selected references and quantitative success claims are accurately reported from primary sources.
Sections 5.1 and Table 1 repeat numbers like GNoME's 2.2M materials and MatterSim's error rates without independent verification; if any primary source is misquoted, the survey inherits the error.
ad hoc to paper The 'systematic' Google Scholar search, restricted to top-tier venues and influential preprints, reflects the field's important work.
Section 1 describes the search but provides no queries, dates, or screening criteria, so the comprehensiveness claim is a judgment call.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools." pith.science (2026). https://pith.science/paper/2IQCX44V

@misc{pith2026250620743,
  author       = {Pith},
  title        = {Pith review of: A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2IQCX44V}},
  note         = {Machine review of arXiv:2506.20743}
}

read the original abstract

Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are typically narrow in scope and require task-specific engineering, FMs offer cross-domain generalization and exhibit emergent capabilities. Their versatility is especially well-suited to materials science, where research challenges span diverse data types and scales. This survey provides a comprehensive overview of foundation models, agentic systems, datasets, and computational tools supporting this growing field. We introduce a task-driven taxonomy encompassing six broad application areas: data extraction, interpretation and Q\&A; atomistic simulation; property prediction; materials structure, design and discovery; process planning, discovery, and optimization; and multiscale modeling. We discuss recent advances in both unimodal and multimodal FMs, as well as emerging large language model (LLM) agents. Furthermore, we review standardized datasets, open-source tools, and autonomous experimental platforms that collectively fuel the development and integration of FMs into research workflows. We assess the early successes of foundation models and identify persistent limitations, including challenges in generalizability, interpretability, data imbalance, safety concerns, and limited multimodal fusion. Finally, we articulate future research directions centered on scalable pretraining, continual learning, data governance, and trustworthiness.

Figures

Figures reproduced from arXiv: 2506.20743 by the authors.

**Figure 2.** An illustrative example of the interplay of foundation models for materials science with data types and [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Development of AI in materials science over time: foundation models, datasets, and tools and infrastructure. [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

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

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents
cs.CL 2026-02 conditional novelty 6.0 of 10

SciAgent-8B, fine-tuned on trajectories synthesized from a tool dependency graph, outperforms Qwen3-VL-235B-Instruct on SciAgentBench, a new 259-task benchmark for multi-step scientific tool-use.
An Encoder-Decoder Foundation Chemical Language Model for Generative Polymer Design
cond-mat.mtrl-sci 2025-10 conditional novelty 5.0 of 10

A T5-based polymer language model pre-trained on 100 million hypothetical polymers predicts thermal, electronic, and solubility properties and generates polymers conditioned on a target glass-transition temperature, w...
The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
cond-mat.mtrl-sci 2026-07 conditional novelty 3.5 of 10

AI for nanoparticle TEM/STEM has progressed from detection and segmentation to physics-informed restoration, 2D-to-3D inference, and spatiotemporal analysis of in situ dynamics, with remaining gaps in benchmarking and...

Reference graph

Works this paper leans on

188 extracted references · 27 canonical work pages · cited by 3 Pith papers

[1]

Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025

Edward O Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R Smith, and Alessandro Curioni. Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025

2025
[2]

Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024

Zhenzhong Wang, Haowei Hua, Wanyu Lin, Ming Yang, and Kay Chen Tan. Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024

arXiv 2024
[3]

Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024

Xiao-Qi Han, Xin-De Wang, Meng-Yuan Xu, Zhen Feng, Bo-Wen Yao, Peng-Jie Guo, Ze-Feng Gao, and Zhong-Yi Lu. Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024

2024
[4]

A review of large language models and autonomous agents in chemistry.Chemical Science, 2025

Mayk Caldas Ramos, Christopher J Collison, and Andrew D White. A review of large language models and autonomous agents in chemistry.Chemical Science, 2025

2025
[5]

A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022

Piyush Karande, Brian Gallagher, and Thomas Yong-Jin Han. A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022

2022
[6]

Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023

Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, and Md Israfil Hossain Rimon. Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023

2023
[7]

Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186, 2019

2019
[8]

Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

2019

Show all 188 references

[9]

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020

1901
[10]

Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023

2023 arXiv
[11]

Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

2023
[12]

Learning transferable visual models from natural language supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InInternational conference on machine learning, pag...

2021
[13]

Emerging properties in self-supervised vision transformers

Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. InProceedings of the IEEE/CVF international conference on computer vision, pages 9650–9660, 2021

2021
[14]

Scaling deep learning for materials discovery.Nature, 624(7990):80–85, 2023

Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk. Scaling deep learning for materials discovery.Nature, 624(7990):80–85, 2023

2023
[15]

Mattersim: A deep learning atomistic model across elements, temperatures and pressures

Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, et al. Mattersim: A deep learning atomistic model across elements, temperatures and pressures. arXiv preprint arXiv:2405.04967, 2024. 22 Survey of AI for MSA PREPRINT

2024 arXiv
[16]

A foundation model for atomistic materials chemistry.arXiv preprint arXiv:2401.00096, 2023

Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M Elena, Dávid P Kovács, Janosh Riebesell, Xavier R Advincula, Mark Asta, Matthew Avaylon, William J Baldwin, et al. A foundation model for atomistic materials chemistry.arXiv preprint arXiv:2401.00096, 2023

2023 arXiv
[17]

Mattergen: a generative model for inorganic materials design

Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, et al. Mattergen: a generative model for inorganic materials design. arXiv preprint arXiv:2312.03687, 2023

2023 arXiv
[18]

Space group constrained crystal generation.arXiv preprint arXiv:2402.03992, 2024

Rui Jiao, Wenbing Huang, Yu Liu, Deli Zhao, and Yang Liu. Space group constrained crystal generation.arXiv preprint arXiv:2402.03992, 2024

2024 arXiv
[19]

Space group informed transformer for crystalline materials generation.arXiv preprint arXiv:2403.15734, 2024

Zhendong Cao, Xiaoshan Luo, Jian Lv, and Lei Wang. Space group informed transformer for crystalline materials generation.arXiv preprint arXiv:2403.15734, 2024

2024
[20]

nach0: multimodal natural and chemical languages foundation model.Chemical Science, 15(22):8380–8389, 2024

Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, Alán Aspuru-Guzik, et al. nach0: multimodal natural and chemical languages foundation model.Chemical Science, 15(22):8380...

2024
[21]

Multimodal foundation models for material property prediction and discovery.Newton, 2025

Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Samuel Kim, Peter Y Lu, Thomas Christensen, and Marin Soljaˇci´c. Multimodal foundation models for material property prediction and discovery.Newton, 2025

2025
[22]

Matterchat: A multi-modal llm for material science.arXiv preprint arXiv:2502.13107, 2025

Yingheng Tang, Wenbin Xu, Jie Cao, Jianzhu Ma, Weilu Gao, Steve Farrell, Benjamin Erichson, Michael W Mahoney, Andy Nonaka, and Zhi Yao. Matterchat: A multi-modal llm for material science.arXiv preprint arXiv:2502.13107, 2025

2025 arXiv
[23]

Atlantic: Structure-aware retrieval-augmented language model for interdisciplinary science.arXiv preprint arXiv:2311.12289, 2023

Sai Munikoti, Anurag Acharya, Sridevi Wagle, and Sameera Horawalavithana. Atlantic: Structure-aware retrieval-augmented language model for interdisciplinary science.arXiv preprint arXiv:2311.12289, 2023

2023 arXiv
[24]

Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):1–16, 2024

Luis M Antunes, Keith T Butler, and Ricardo Grau-Crespo. Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):1–16, 2024

2024
[25]

Accelerating material design with the generative toolkit for scientific discovery.npj Computational Materials, 9(1):69, 2023

Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gae- tan Nana Teukam, Giorgio Giannone, Samuel C Hoffman, Matthew Buchan, et al. Accelerating material design with the generative toolkit for scientific discovery.npj Computation...

2023
[26]

An autonomous laboratory for the accelerated synthesis of novel materials.Nature, 624(7990):86–91, 2023

Nathan J Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E Kumar, Tanjin He, David Milsted, Matthew J McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, et al. An autonomous laboratory for the accelerated synthesis of novel materials.Nature, 624(7990):86–91, 2023

2023
[27]

Developing a foundation model for predicting material failure

Agnese Marcato, Javier E Santos, Aleksandra Pachalieva, Kai Gao, Ryley Hill, Esteban Rougier, Qinjun Kang, Jeffrey Hyman, Abigail Hunter, Janel Chua, et al. Developing a foundation model for predicting material failure. arXiv preprint arXiv:2411.08354, 2024

2024 arXiv
[28]

Foundation models for the process industry: Challenges and opportunities.Engineering, 2025

Lei Ren, Haiteng Wang, Yuqing Wang, Keke Huang, Lihui Wang, and Bohu Li. Foundation models for the process industry: Challenges and opportunities.Engineering, 2025

2025
[29]

A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518, 2025

Junyoung Choi, Gunwook Nam, Jaesik Choi, and Yousung Jung. A perspective on foundation models in chemistry.JACS Au, 5(4):1499–1518, 2025

2025
[30]

Towards foundation models for materials science: The open matsci ml toolkit

Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings, Mikhail Galkin, Santiago Miret, and Nalini Kumar. Towards foundation models for materials science: The open matsci ml toolkit. InProceedings of the SC’23 Workshops of the International Conference on High Performance Com...

2023
[31]

Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024

Kamal Choudhary. Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024

2024
[32]

Multi-view mixture-of-experts for predicting molecular properties using smiles, selfies, and graph-based representations

Eduardo Soares, Indra Priyadarsini, Emilio Vital Brazil, Victor Yukio Shirasuna, and Seiji Takeda. Multi-view mixture-of-experts for predicting molecular properties using smiles, selfies, and graph-based representations. In Neurips 2024 Workshop Foundation Models for Science: ...

2024
[33]

Chemdfm: A large language foundation model for chemistry.arXiv preprint arXiv:2401.14818, 2024

Zihan Zhao, Da Ma, Lu Chen, Liangtai Sun, Zihao Li, Yi Xia, Bo Chen, Hongshen Xu, Zichen Zhu, Su Zhu, et al. Chemdfm: A large language foundation model for chemistry.arXiv preprint arXiv:2401.14818, 2024

2024 arXiv
[34]

Foundational large language models for materials research.arXiv preprint arXiv:2412.09560, 2024

Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat, Mohd Zaki, Vaibhav Bihani, Hargun Singh Grover, Biswajit Mishra, Santiago Miret, NM Krishnan, et al. Foundational large language models for materials research.arXiv preprint arXiv:2412.09560, 2024

2024 arXiv
[35]

Scitune: Aligning large language models with scientific multimodal instructions.arXiv preprint arXiv:2307.01139, 2023

Sameera Horawalavithana, Sai Munikoti, Ian Stewart, and Henry Kvinge. Scitune: Aligning large language models with scientific multimodal instructions.arXiv preprint arXiv:2307.01139, 2023. 23 Survey of AI for MSA PREPRINT

2023 arXiv
[36]

Honeycomb: A flexible llm-based agent system for materials science.arXiv preprint arXiv:2409.00135, 2024

Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, and Bang Liu. Honeycomb: A flexible llm-based agent system for materials science.arXiv preprint arXiv:2409.00135, 2024

2024 arXiv
[37]

Llmatdesign: Autonomous materials discovery with large language models.arXiv preprint arXiv:2406.13163, 2024

Shuyi Jia, Chao Zhang, and Victor Fung. Llmatdesign: Autonomous materials discovery with large language models.arXiv preprint arXiv:2406.13163, 2024

2024 arXiv
[38]

Chatmof: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications, 15(1):4705, 2024

Yeonghun Kang and Jihan Kim. Chatmof: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications, 15(1):4705, 2024

2024
[39]

Matagent: A human-in-the-loop multi-agent llm framework for accelerating the material science discovery cycle

Adib Bazgir, Yuwen Zhang, et al. Matagent: A human-in-the-loop multi-agent llm framework for accelerating the material science discovery cycle. InAI for Accelerated Materials Design-ICLR 2025

2025
[40]

Matpilot: an llm-enabled ai materials scientist under the framework of human-machine collaboration.arXiv preprint arXiv:2411.08063, 2024

Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen, Fengqi Liu, Yicong Ye, and Shuxin Bai. Matpilot: an llm-enabled ai materials scientist under the framework of human-machine collaboration.arXiv preprint arXiv:2411.08063, 2024

2024 arXiv
[41]

Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller

Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Augmenting large language models with chemistry tools.Nature Machine Intelligence, 6(5):525–535, 2024

2024
[42]

The open matsci ml toolkit: A flexible framework for machine learning in materials science.arXiv preprint arXiv:2210.17484, 2022

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, and Matthew Spellings. The open matsci ml toolkit: A flexible framework for machine learning in materials science.arXiv preprint arXiv:2210.17484, 2022

2022 arXiv
[43]

Forge: Pre-training open foundation models for science

Junqi Yin, Sajal Dash, Feiyi Wang, and Mallikarjun Shankar. Forge: Pre-training open foundation models for science. InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–13, 2023

2023
[44]

Attention is all you need.Advances in neural information processing systems, 30, 2017

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

2017
[45]

Improving language understanding by generative pre-training

Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018

2018
[46]

Gemini: a family of highly capable multimodal models

Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalk- wyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023

2023 arXiv
[47]

Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

2023 arXiv
[48]

Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.arXiv preprint arXiv:1910.13461, 2019

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.arXiv preprint arXiv:1910.13461, 2019

1910 arXiv
[49]

React: Synergizing reasoning and acting in language models

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InInternational Conference on Learning Representations (ICLR), 2023

2023
[50]

Llm-planner: Few-shot grounded planning for embodied agents with large language models

Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. Llm-planner: Few-shot grounded planning for embodied agents with large language models. InProceedings of the IEEE/CVF international conference on computer vision, pages 2998–3009, 2023

2023
[51]

Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

2023
[52]

Language models as zero-shot planners: Extracting actionable knowledge for embodied agents

Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. InInternational conference on machine learning, pages 9118–9147. PMLR, 2022

2022
[53]

Theory of mind may have spontaneously emerged in large language models.arXiv preprint arXiv:2302.02083, 4:169, 2023

Michal Kosinski. Theory of mind may have spontaneously emerged in large language models.arXiv preprint arXiv:2302.02083, 4:169, 2023

2023 arXiv
[54]

Role play with large language models.Nature, 623 (7987):493–498, 2023

Murray Shanahan, Kyle McDonell, and Laria Reynolds. Role play with large language models.Nature, 623 (7987):493–498, 2023

2023
[55]

A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

2024
[56]

Honeybee: Progressive instruction finetuning of large language models for materials science.arXiv preprint arXiv:2310.08511, 2023

Yu Song, Santiago Miret, Huan Zhang, and Bang Liu. Honeybee: Progressive instruction finetuning of large language models for materials science.arXiv preprint arXiv:2310.08511, 2023. 24 Survey of AI for MSA PREPRINT

2023 arXiv
[57]

Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018

Tian Xie and Jeffrey C Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.Physical review letters, 120(14):145301, 2018

2018
[58]

Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

2019
[59]

Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models.ACS Catalysis, 13(24):16032–16044, 2023

Janghoon Ock, Chakradhar Guntuboina, and Amir Barati Farimani. Catalyst energy prediction with catberta: Unveiling feature exploration strategies through large language models.ACS Catalysis, 13(24):16032–16044, 2023

2023
[60]

Molxpt: Wrapping molecules with text for generative pre-training.arXiv preprint arXiv:2305.10688, 2023

Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and Tie-Yan Liu. Molxpt: Wrapping molecules with text for generative pre-training.arXiv preprint arXiv:2305.10688, 2023

2023 arXiv
[61]

Crystal diffusion variational autoencoder for periodic material generation.arXiv preprint arXiv:2110.06197, 2021

Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola. Crystal diffusion variational autoencoder for periodic material generation.arXiv preprint arXiv:2110.06197, 2021

2021 arXiv
[62]

Crystal structure prediction by joint equivariant diffusion.Advances in Neural Information Processing Systems, 36:17464–17497, 2023

Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu. Crystal structure prediction by joint equivariant diffusion.Advances in Neural Information Processing Systems, 36:17464–17497, 2023

2023
[63]

Gp-molformer: A foundation model for molecular generation.arXiv preprint arXiv:2405.04912, 2024

Jerret Ross, Brian Belgodere, Samuel C Hoffman, Vijil Chenthamarakshan, Jiri Navratil, Youssef Mroueh, and Payel Das. Gp-molformer: A foundation model for molecular generation.arXiv preprint arXiv:2405.04912, 2024

2024 arXiv
[64]

Mattergpt: A generative transformer for multi-property inverse design of solid-state materials.arXiv preprint arXiv:2408.07608, 2024

Yan Chen, Xueru Wang, Xiaobin Deng, Yilun Liu, Xi Chen, Yunwei Zhang, Lei Wang, and Hang Xiao. Mattergpt: A generative transformer for multi-property inverse design of solid-state materials.arXiv preprint arXiv:2408.07608, 2024

2024 arXiv
[65]

Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, 2022

Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology, 3(1):015022, 2022

2022
[66]

Flowllm: Flow matching for material generation with large language models as base distributions.Advances in Neural Information Processing Systems, 37:46025–46046, 2024

Anuroop Sriram, Benjamin Miller, Ricky TQ Chen, and Brandon Wood. Flowllm: Flow matching for material generation with large language models as base distributions.Advances in Neural Information Processing Systems, 37:46025–46046, 2024

2024
[67]

Is large language model all you need to predict the synthesizability and precursors of crystal structures?arXiv preprint arXiv:2407.07016, 2024

Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou, and Jinlan Wang. Is large language model all you need to predict the synthesizability and precursors of crystal structures?arXiv preprint arXiv:2407.07016, 2024

2024 arXiv
[68]

Llm-fusion: A novel multimodal fusion model for accelerated material discovery.arXiv preprint arXiv:2503.01022, 2025

Onur Boyar, Indra Priyadarsini, Seiji Takeda, and Lisa Hamada. Llm-fusion: A novel multimodal fusion model for accelerated material discovery.arXiv preprint arXiv:2503.01022, 2025

2025 arXiv
[69]

Unifying molecular and textual representations via multi-task language modelling

Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. Unifying molecular and textual representations via multi-task language modelling. InInternational Conference on Machine Learning, pages 6140–6157. PMLR, 2023

2023
[70]

Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns, 3(4), 2022

Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patter...

2022
[71]

Matscibert: A materials domain language model for text mining and information extraction.npj Computational Materials, 8(1):102, 2022

Tanishq Gupta, Mohd Zaki, NM Anoop Krishnan, and Mausam. Matscibert: A materials domain language model for text mining and information extraction.npj Computational Materials, 8(1):102, 2022

2022
[72]

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.npj Computational Materials, 9(1):52, 2023

Pranav Shetty, Arunkumar Chitteth Rajan, Chris Kuenneth, Sonakshi Gupta, Lakshmi Prerana Panchumarti, Lauren Holm, Chao Zhang, and Rampi Ramprasad. A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.npj Com...

2023
[73]

Molscribe: robust molecular structure recognition with image-to-graph generation.Journal of Chemical Information and Modeling, 63(7):1925–1934, 2023

Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W Coley, and Regina Barzilay. Molscribe: robust molecular structure recognition with image-to-graph generation.Journal of Chemical Information and Modeling, 63(7):1925–1934, 2023

1925
[74]

Deplot: One-shot visual language reasoning by plot-to- table translation.arXiv preprint arXiv:2212.10505, 2022

Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. Deplot: One-shot visual language reasoning by plot-to- table translation.arXiv preprint arXiv:2212.10505, 2022

2022 arXiv
[75]

Mascqa: investigating materials science knowledge of large language models.Digital Discovery, 3(2):313–327, 2024

Mohd Zaki, NM Anoop Krishnan, et al. Mascqa: investigating materials science knowledge of large language models.Digital Discovery, 3(2):313–327, 2024

2024
[76]

Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science, 16(2):670–684, 2025

Joren Van Herck, María Victoria Gil, Kevin Maik Jablonka, Alex Abrudan, Andy S Anker, Mehrdad Asgari, Ben Blaiszik, Antonio Buffo, Leander Choudhury, Clemence Corminboeuf, et al. Assessment of fine-tuned large language models for real-world chemistry and material science appli...

2025
[77]

Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021

Ben Wang and Aran Komatsuzaki. Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021. 25 Survey of AI for MSA PREPRINT

2021
[78]

The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

2024 arXiv
[79]

Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas ...

2023 arXiv
[80]

Smiles, a chemical language and information system

David Weininger. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules.Journal of chemical information and computer sciences, 28(1):31–36, 1988

1988
[81]

Uni-smart: Universal science multimodal analysis and research transformer

Hengxing Cai, Xiaochen Cai, Shuwen Yang, Jiankun Wang, Lin Yao, Zhifeng Gao, Junhan Chang, Sihang Li, Mingjun Xu, Changxin Wang, et al. Uni-smart: Universal science multimodal analysis and research transformer. arXiv preprint arXiv:2403.10301, 2024

2024 arXiv
[82]

The claude 3 model family: Opus, sonnet, haiku

Anthropic. The claude 3 model family: Opus, sonnet, haiku. 2025. URL https://api.semanticscholar. org/CorpusID:268232499

2025
[83]

Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

2021
[84]

Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.Advances in neural information processing systems, 35:11423–11436, 2022

Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.Advances in neural information processing systems, 35:11423–11436, 2022

2022
[85]

Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical science, 8(4):3192–3203, 2017

Justin S Smith, Olexandr Isayev, and Adrian E Roitberg. Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical science, 8(4):3192–3203, 2017

2017
[86]

Generalized neural-network representation of high-dimensional potential- energy surfaces.Physical review letters, 98(14):146401, 2007

Jörg Behler and Michele Parrinello. Generalized neural-network representation of high-dimensional potential- energy surfaces.Physical review letters, 98(14):146401, 2007

2007
[87]

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.Science advances, 5(8):eaav6490, 2019

Roman Zubatyuk, Justin S Smith, Jerzy Leszczynski, and Olexandr Isayev. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.Science advances, 5(8):eaav6490, 2019

2019
[88]

Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 2025

Dylan M Anstine, Roman Zubatyuk, and Olexandr Isayev. Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.Chemical Science, 2025

2025
[89]

The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

2008
[90]

Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024, 2020

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik. Self-referencing embedded strings (selfies): A 100% robust molecular string representation.Machine Learning: Science and Technology, 1(4):045024, 2020

2020
[91]

Zinc 15–ligand discovery for everyone.Journal of chemical information and modeling, 55(11):2324–2337, 2015

Teague Sterling and John J Irwin. Zinc 15–ligand discovery for everyone.Journal of chemical information and modeling, 55(11):2324–2337, 2015

2015
[92]

Zinc20—a free ultralarge-scale chemical database for ligand discovery.Journal of chemical information and modeling, 60(12):6065–6073, 2020

John J Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong, Munkhzul Khurel- baatar, Yurii S Moroz, John Mayfield, and Roger A Sayle. Zinc20—a free ultralarge-scale chemical database for ligand discovery.Journal of chemical information and modeling, 6...

2020
[93]

The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1): D1180–D1192, 2024

Barbara Zdrazil, Eloy Felix, Fiona Hunter, Emma J Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij, Harris Ioannidis, David Mendez Lopez, Juan F Mosquera, et al. The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time ...

2023
[94]

Chembl web services: streamlining access to drug discovery data and utilities

Mark Davies, Michał Nowotka, George Papadatos, Nathan Dedman, Anna Gaulton, Francis Atkinson, Louisa Bellis, and John P Overington. Chembl web services: streamlining access to drug discovery data and utilities. Nucleic acids research, 43(W1):W612–W620, 2015

2015
[95]

Schnet–a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018

Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller. Schnet–a deep learning architecture for molecules and materials.The Journal of Chemical Physics, 148(24), 2018

2018
[96]

Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materials, 1(1), 2013

Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materia...

2013
[97]

Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17.Journal of chemical information and modeling, 52(11):2864–2875, 2012

Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond. Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17.Journal of chemical information and modeling, 52(11):2864–2875, 2012

2012
[98]

Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole V on Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

2014
[99]

A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020

Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer. A compact review of molecular property prediction with graph neural networks.Drug Discovery Today: Technologies, 37:1–12, 2020

2020
[100]

Chemberta: large-scale self-supervised pretraining for molecular property prediction.arXiv preprint arXiv:2010.09885, 2020

Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. Chemberta: large-scale self-supervised pretraining for molecular property prediction.arXiv preprint arXiv:2010.09885, 2020

2010 arXiv
[101]

Mol-bert: An effective molecular representation with bert for molecular property prediction.Wireless Communications and Mobile Computing, 2021(1):7181815, 2021

Juncai Li and Xiaofei Jiang. Mol-bert: An effective molecular representation with bert for molecular property prediction.Wireless Communications and Mobile Computing, 2021(1):7181815, 2021

2021
[102]

Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, 2019

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, 2019

1907 arXiv
[103]

Pubchem 2025 update.Nucleic Acids Research, 53(D1):D1516– D1525, 2025

Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al. Pubchem 2025 update.Nucleic Acids Research, 53(D1):D1516– D1525, 2025

2025
[104]

Open catalyst 2020 (oc20) dataset and community challenges.Acs Catalysis, 11(10):6059–6072, 2021

Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al. Open catalyst 2020 (oc20) dataset and community challenges.Acs Catalysis, 11(10):6059–6072, 2021

2020
[105]

Selformer: molecular representation learning via selfies language models.Machine Learning: Science and Technology, 4(2):025035, 2023

Atakan Yüksel, Erva Ulusoy, Atabey Ünlü, and Tunca Do˘gan. Selformer: molecular representation learning via selfies language models.Machine Learning: Science and Technology, 4(2):025035, 2023

2023
[106]

Periodic graph transformers for crystal material property prediction.Advances in Neural Information Processing Systems, 35:15066–15080, 2022

Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji. Periodic graph transformers for crystal material property prediction.Advances in Neural Information Processing Systems, 35:15066–15080, 2022

2022
[107]

Complete and efficient graph transformers for crystal material property prediction.arXiv preprint arXiv:2403.11857, 2024

Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji. Complete and efficient graph transformers for crystal material property prediction.arXiv preprint arXiv:2403.11857, 2024

2024 arXiv
[108]

Generative pre-training from molecules

Sanjar Adilov. Generative pre-training from molecules. 2021

2021
[109]

PubMed Database

National Library of Medicine (US). PubMed Database. https://pubmed.ncbi.nlm.nih.gov/, 2024. Ac- cessed: 2025-06-09

2024
[110]

A smile is all you need: predicting limiting activity coefficients from smiles with natural language processing.Digital Discovery, 1(6):859–869, 2022

Benedikt Winter, Clemens Winter, Johannes Schilling, and André Bardow. A smile is all you need: predicting limiting activity coefficients from smiles with natural language processing.Digital Discovery, 1(6):859–869, 2022

2022
[111]

Leveraging large language models for predictive chemistry.Nature Machine Intelligence, 6(2):161–169, 2024

Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit. Leveraging large language models for predictive chemistry.Nature Machine Intelligence, 6(2):161–169, 2024

2024
[112]

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.Advances in neural information processing systems, 32, 2019

Niklas Gebauer, Michael Gastegger, and Kristof Schütt. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules.Advances in neural information processing systems, 32, 2019

2019
[113]

Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

2020
[114]

Auto-encoding variational bayes, 2013

Diederik P Kingma, Max Welling, et al. Auto-encoding variational bayes, 2013

2013
[115]

An introduction to variational autoencoders.Foundations and Trends® in Machine Learning, 12(4):307–392, 2019

Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders.Foundations and Trends® in Machine Learning, 12(4):307–392, 2019

2019
[116]

Con-cdvae: A method for the conditional generation of crystal structures.Computational Materials Today, 1:100003, 2024

Cai-Yuan Ye, Hong-Ming Weng, and Quan-Sheng Wu. Con-cdvae: A method for the conditional generation of crystal structures.Computational Materials Today, 1:100003, 2024

2024
[117]

An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.Nature Communications, 14(1):7027, 2023

Hang Xiao, Rong Li, Xiaoyang Shi, Yan Chen, Liangliang Zhu, Xi Chen, and Lei Wang. An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.Nature Communications, 14(1):7027, 2023

2023
[118]

Fine-tuned language models generate stable inorganic materials as text.arXiv preprint arXiv:2402.04379, 2024

Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C Lawrence Zitnick, and Zachary Ulissi. Fine-tuned language models generate stable inorganic materials as text.arXiv preprint arXiv:2402.04379, 2024

2024 arXiv
[119]

Language models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files.arXiv preprint arXiv:2305.05708, 2023

Daniel Flam-Shepherd and Alán Aspuru-Guzik. Language models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files.arXiv preprint arXiv:2305.05708, 2023. 27 Survey of AI for MSA PREPRINT

2023 arXiv
[120]

Jarvis: An integrated infrastructure for data-driven materials design.Preprint at, https://arxiv

Kamal Choudhary, Kevin F Garrity, Andrew CE Reid, Brian DeCost, Adam J Biacchi, Angela R Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A Gilad Kusne, Andrea Centrone, et al. Jarvis: An integrated infrastructure for data-driven materials design.Preprint at, https://arxi...

2007 arXiv
[121]

Large language models are innate crystal structure generators

Jingru Gan, Peichen Zhong, Yuanqi Du, Yanqiao Zhu, Chenru Duan, Haorui Wang, Carla P Gomes, Kristin A Persson, Daniel Schwalbe-Koda, and Wei Wang. Large language models are innate crystal structure generators. arXiv preprint arXiv:2502.20933, 2025

2025
[122]

Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction.ACS central science, 5(9):1572–1583, 2019

Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction.ACS central science, 5(9):1572–1583, 2019

2019
[123]

Uspto – open data portal.https://data.uspto.gov/home, 2025

USPTO. Uspto – open data portal.https://data.uspto.gov/home, 2025

2025
[124]

Esol: estimating aqueous solubility directly from molecular structure.Journal of chemical information and computer sciences, 44(3):1000–1005, 2004

John S Delaney. Esol: estimating aqueous solubility directly from molecular structure.Journal of chemical information and computer sciences, 44(3):1000–1005, 2004

2004
[125]

Matchat: A large language model and application service platform for materials science.Chinese Physics B, 32(11):118104, 2023

Zi-Yi Chen, Fan-Kai Xie, Meng Wan, Yang Yuan, Miao Liu, Zong-Guo Wang, Sheng Meng, and Yan-Gang Wang. Matchat: A large language model and application service platform for materials science.Chinese Physics B, 32(11):118104, 2023

2023
[126]

Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

2022
[127]

Synask: unleashing the power of large language models in organic synthesis.Chemical Science, 16(1):43–56, 2025

Chonghuan Zhang, Qianghua Lin, Biwei Zhu, Haopeng Yang, Xiao Lian, Hao Deng, Jiajun Zheng, and Kuangbiao Liao. Synask: unleashing the power of large language models in organic synthesis.Chemical Science, 16(1):43–56, 2025

2025
[128]

Are llms ready for real-world materials discovery?arXiv preprint arXiv:2402.05200, 2024

Santiago Miret and Nandan M Krishnan. Are llms ready for real-world materials discovery?arXiv preprint arXiv:2402.05200, 2024

2024 arXiv
[129]

Mesoscopic and multiscale modelling in materials.Nature materials, 20(6):774–786, 2021

Jacob Fish, Gregory J Wagner, and Sinan Keten. Mesoscopic and multiscale modelling in materials.Nature materials, 20(6):774–786, 2021

2021
[130]

Machine learning-driven multiscale modeling: bridging the scales with a next-generation simulation infrastructure.Journal of Chemical Theory and Computation, 19(9):2658–2675, 2023

Helgi I Ingólfsson, Harsh Bhatia, Fikret Aydin, Tomas Oppelstrup, Cesar A López, Liam G Stanton, Timothy S Carpenter, Sergio Wong, Francesco Di Natale, Xiaohua Zhang, et al. Machine learning-driven multiscale modeling: bridging the scales with a next-generation simulation infr...

2023
[131]

Efficient multiscale modeling of heterogeneous materials using deep neural networks.Computational Mechanics, 72(1):155–171, 2023

Fadi Aldakheel, Elsayed S Elsayed, Tarek I Zohdi, and Peter Wriggers. Efficient multiscale modeling of heterogeneous materials using deep neural networks.Computational Mechanics, 72(1):155–171, 2023

2023
[132]

Pistachio: Search and faceting of large reaction databases

John Mayfield, Daniel Lowe, and Roger Sayle. Pistachio: Search and faceting of large reaction databases. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 254. AMER CHEMICAL SOC 1155 16TH ST, NW, W ASHINGTON, DC 20036 USA, 2017

2017
[133]

Text2mol: Cross-modal molecule retrieval with natural language queries

Carl Edwards, ChengXiang Zhai, and Heng Ji. Text2mol: Cross-modal molecule retrieval with natural language queries. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 595–607, 2021

2021
[134]

Regression transformer enables concurrent sequence regression and generation for molecular language modelling.Nature Machine Intelligence, 5(4):432–444, 2023

Jannis Born and Matteo Manica. Regression transformer enables concurrent sequence regression and generation for molecular language modelling.Nature Machine Intelligence, 5(4):432–444, 2023

2023
[135]

Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2): 513–530, 2018

Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2): 513–530, 2018

2018
[136]

Evaluating protein transfer learning with tape.Advances in neural information processing systems, 32, 2019

Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. Evaluating protein transfer learning with tape.Advances in neural information processing systems, 32, 2019

2019
[137]

A large encoder-decoder family of foundation models for chemical language.arXiv preprint arXiv:2407.20267, 2024

Eduardo Soares, Victor Shirasuna, Emilio Vital Brazil, Renato Cerqueira, Dmitry Zubarev, and Kristin Schmidt. A large encoder-decoder family of foundation models for chemical language.arXiv preprint arXiv:2407.20267, 2024

2024 arXiv
[138]

Self-bart: A transformer-based molecular representation model using selfies.arXiv preprint arXiv:2410.12348, 2024

Indra Priyadarsini, Seiji Takeda, Lisa Hamada, Emilio Vital Brazil, Eduardo Soares, and Hajime Shinohara. Self-bart: A transformer-based molecular representation model using selfies.arXiv preprint arXiv:2410.12348, 2024

2024 arXiv
[139]

Mhg-gnn: Combination of molecular hypergraph grammar with graph neural network.arXiv preprint arXiv:2309.16374, 2023

Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, and Seiji Takeda. Mhg-gnn: Combination of molecular hypergraph grammar with graph neural network.arXiv preprint arXiv:2309.16374, 2023. 28 Surv...

2023 arXiv
[140]

Improvements to bm25 and language models examined

Andrew Trotman, Antti Puurula, and Blake Burgess. Improvements to bm25 and language models examined. In Proceedings of the 19th Australasian Document Computing Symposium, pages 58–65, 2014

2014
[141]

Unsupervised dense information retrieval with contrastive learning.arXiv preprint arXiv:2112.09118, 2021

Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning.arXiv preprint arXiv:2112.09118, 2021

2021 arXiv
[142]

Benchmarking graph neural networks for materials chemistry.npj Computational Materials, 7(1):84, 2021

Victor Fung, Jiaxin Zhang, Eric Juarez, and Bobby G Sumpter. Benchmarking graph neural networks for materials chemistry.npj Computational Materials, 7(1):84, 2021

2021
[143]

Torchmd-net: equivariant transformers for neural network based molecular potentials.arXiv preprint arXiv:2202.02541, 2022

Philipp Thölke and Gianni De Fabritiis. Torchmd-net: equivariant transformers for neural network based molecular potentials.arXiv preprint arXiv:2202.02541, 2022

2022 arXiv
[144]

Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features.Applied Crystallography, 52(5): 918–925, 2019

Dejan Zagorac, H Müller, S Ruehl, J Zagorac, and Silke Rehme. Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features.Applied Crystallography, 52(5): 918–925, 2019

2019
[145]

The open quantum materials database (oqmd): assessing the accuracy of dft formation energies.npj Computational Materials, 1(1):1–15, 2015

Scott Kirklin, James E Saal, Bryce Meredig, Alex Thompson, Jeff W Doak, Muratahan Aykol, Stephan Rühl, and Chris Wolverton. The open quantum materials database (oqmd): assessing the accuracy of dft formation energies.npj Computational Materials, 1(1):1–15, 2015

2015
[146]

Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd).Jom, 65:1501–1509, 2013

James E Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and Christopher Wolverton. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd).Jom, 65:1501–1509, 2013

2013
[147]

The nomad laboratory: from data sharing to artificial intelligence.Journal of Physics: Materials, 2(3):036001, 2019

Claudia Draxl and Matthias Scheffler. The nomad laboratory: from data sharing to artificial intelligence.Journal of Physics: Materials, 2(3):036001, 2019

2019
[148]

Open materials 2024 (omat24) inorganic materials dataset and models.arXiv preprint arXiv:2410.12771, 2024

Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C Lawrence Zitnick, and Zachary W Ulissi. Open materials 2024 (omat24) inorganic materials dataset and models.arXiv preprint arXiv:2410.12771, 2024

2024 arXiv
[149]

Snumat.https://www.snumat.com/apis, 2025

SNU MDIL. Snumat.https://www.snumat.com/apis, 2025

2025
[150]

Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling

Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder. Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence, 5(9):1031–1041, 2023

2023
[151]

Improving machine-learning models in materials science through large datasets

Jonathan Schmidt, Tiago FT Cerqueira, Aldo H Romero, Antoine Loew, Fabian Jäger, Hai-Chen Wang, Silvana Botti, and Miguel AL Marques. Improving machine-learning models in materials science through large datasets. Materials Today Physics, 48:101560, 2024

2024
[152]

Guacamol: benchmarking models for de novo molecular design.Journal of chemical information and modeling, 59(3):1096–1108, 2019

Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher. Guacamol: benchmarking models for de novo molecular design.Journal of chemical information and modeling, 59(3):1096–1108, 2019

2019
[153]

Molecular sets (moses): a benchmarking platform for molecular generation models.Frontiers in pharmacology, 11:565644, 2020

Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al. Molecular sets (moses): a benchmarking platform for molecular generation models.Fr...

2020
[154]

Unsupervised word embeddings capture latent knowledge from materials science literature.Nature, 571(7763):95–98, 2019

Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain. Unsupervised word embeddings capture latent knowledge from materials science literature.Nature, 571(7763):95–98, 2019

2019
[155]

Leigh Weston, Vahe Tshitoyan, John Dagdelen, Olga Kononova, Amalie Trewartha, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain. Named entity recognition and normalization applied to large-scale information extraction from the materials science literature.Journal of chemical...

2019
[156]

Matsci-nlp: Evaluating scientific language models on materials science language tasks using text-to-schema modeling.arXiv preprint arXiv:2305.08264, 2023

Yu Song, Santiago Miret, and Bang Liu. Matsci-nlp: Evaluating scientific language models on materials science language tasks using text-to-schema modeling.arXiv preprint arXiv:2305.08264, 2023

2023 arXiv
[157]

Llm4mat-bench: Benchmarking large language models for materials property prediction.Machine Learning: Science and Technology, 2024

Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers, and Adji Bousso Dieng. Llm4mat-bench: Benchmarking large language models for materials property prediction.Machine Learning: Science and Technology, 2024

2024
[158]

Benchmarking large language models for molecule prediction tasks.arXiv preprint arXiv:2403.05075, 2024

Zhiqiang Zhong, Kuangyu Zhou, and Davide Mottin. Benchmarking large language models for molecule prediction tasks.arXiv preprint arXiv:2403.05075, 2024

2024 arXiv
[159]

MaCBench: A multimodal chemistry and materi- als science benchmark

Nawaf Alampara, Indrajeet Mandal, Pranav Khetarpal, Hargun Singh Grover, Mara Schilling-Wilhelmi, N M Anoop Krishnan, and Kevin Maik Jablonka. MaCBench: A multimodal chemistry and materi- als science benchmark. InAI for Accelerated Materials Design - NeurIPS 2024, 2024. URL ht...

2024
[160]

Redpajama: an open dataset for training large language models.Advances in neural information processing systems, 37:116462–116492, 2024

Maurice Weber, Dan Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, et al. Redpajama: an open dataset for training large language models.Advances in neural information processing systems, 37:116462–116492, 2024

2024
[161]

Aligning books and movies: Towards story-like visual explanations by watching movies and reading books

Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer ...

2015
[162]

Pointer sentinel mixture models.arXiv preprint arXiv:1609.07843, 2016

Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models.arXiv preprint arXiv:1609.07843, 2016

2016 arXiv
[163]

Common crawl dataset, 2025

Common Crawl Team. Common crawl dataset, 2025. URLhttps://commoncrawl.org/

2025
[164]

Specter: Document-level representation learning using citation-informed transformers.arXiv preprint arXiv:2004.07180, 2020

Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S Weld. Specter: Document-level representation learning using citation-informed transformers.arXiv preprint arXiv:2004.07180, 2020

2004 arXiv
[165]

Scicap: Generating captions for scientific figures

Ting-Yao Hsu, C Lee Giles, and Ting-Hao’Kenneth’ Huang. Scicap: Generating captions for scientific figures. arXiv preprint arXiv:2110.11624, 2021

2021 arXiv
[166]

Orca: Progressive learning from complex explanation traces of gpt-4.arXiv preprint arXiv:2306.02707, 2023

Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. Orca: Progressive learning from complex explanation traces of gpt-4.arXiv preprint arXiv:2306.02707, 2023

2023 arXiv
[167]

Mammoth2: Scaling instructions from the web

Xiang Yue, Tianyu Zheng, Ge Zhang, and Wenhu Chen. Mammoth2: Scaling instructions from the web. Advances in Neural Information Processing Systems, 37:90629–90660, 2024

2024
[168]

Mathqa: Towards interpretable math word problem solving with operation-based formalisms.arXiv preprint arXiv:1905.13319, 2019

Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. Mathqa: Towards interpretable math word problem solving with operation-based formalisms.arXiv preprint arXiv:1905.13319, 2019

1905 arXiv
[169]

Think you have solved question answering? try arc, the ai2 reasoning challenge.arXiv preprint arXiv:1803.05457, 2018

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge.arXiv preprint arXiv:1803.05457, 2018

2018 arXiv
[170]

Piqa: Reasoning about physical commonsense in natural language

Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about physical commonsense in natural language. InProceedings of the AAAI conference on artificial intelligence, volume 34, pages 7432–7439, 2020

2020
[171]

Crowdsourcing multiple choice science questions.arXiv preprint arXiv:1707.06209, 2017

Johannes Welbl, Nelson F Liu, and Matt Gardner. Crowdsourcing multiple choice science questions.arXiv preprint arXiv:1707.06209, 2017

2017 arXiv
[172]

Learn to explain: Multimodal reasoning via thought chains for science question answering

Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507–2521, 2022

2022
[173]

Optimade, an api for exchanging materials data.Scientific data, 8(1):217, 2021

Casper W Andersen, Rickard Armiento, Evgeny Blokhin, Gareth J Conduit, Shyam Dwaraknath, Matthew L Evans, Ádám Fekete, Abhijith Gopakumar, Saulius Gražulis, Andrius Merkys, et al. Optimade, an api for exchanging materials data.Scientific data, 8(1):217, 2021

2021
[174]

Python materials genomics (pymatgen): A robust, open-source python library for materials analysis.Computational Materials Science, 68:314–319, 2013

Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder. Python materials genomics (pymatgen): A robust, open-source python library for materials analysis.C...

2013
[175]

M 2hub: Unlocking the potential of machine learning for materials discovery

Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John Gregoire, and Carla P Gomes. M 2hub: Unlocking the potential of machine learning for materials discovery. Advances in Neural Information Processing Systems, 36:77359–77378, 2023

2023
[176]

Fireworks: a dynamic workflow system designed for high-throughput applications.Concurrency and Computation: Practice and Experience, 27(17):5037–5059, 2015

Anubhav Jain, Shyue Ping Ong, Wei Chen, Bharat Medasani, Xiaohui Qu, Michael Kocher, Miriam Brafman, Guido Petretto, Gian-Marco Rignanese, Geoffroy Hautier, et al. Fireworks: a dynamic workflow system designed for high-throughput applications.Concurrency and Computation: Pract...

2015
[177]

Maggma Toolkit.https://materialsproject.github.io/maggma/, 2025

The Materials Project. Maggma Toolkit.https://materialsproject.github.io/maggma/, 2025

2025
[178]

Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm.npj Computational Materials, 6(1):138, 2020

Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain. Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm.npj Computational Materials, 6(1):138, 2020

2020
[179]

Unified graph neural network force-field for the periodic table: solid state applications.Digital Discovery, 2(2): 346–355, 2023

Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, and Francesca Tavazza. Unified graph neural network force-field for the periodic table: solid state applications.Digital Discovery, 2(2): 346–355, 2023. 30 Survey of AI for MSA PREPRINT

2023
[180]

Ollama tool.https://ollama.com/, 2025

Ollama. Ollama tool.https://ollama.com/, 2025

2025
[181]

Langchain tool.https://github.com/langchain-ai/langchain, 2025

LangChain. Langchain tool.https://github.com/langchain-ai/langchain, 2025

2025
[182]

Crystal toolkit: A web app framework to improve usability and accessibility of materials science research algorithms.arXiv preprint arXiv:2302.06147, 2023

Matthew Horton, Jimmy-Xuan Shen, Jordan Burns, Orion Cohen, François Chabbey, Alex M Ganose, Rishabh Guha, Patrick Huck, Hamming Howard Li, Matthew McDermott, et al. Crystal toolkit: A web app framework to improve usability and accessibility of materials science research algor...

2023 arXiv
[183]

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows.Computational Materials Science, 139:140–152, 2017

Kiran Mathew, Joseph H Montoya, Alireza Faghaninia, Shyam Dwarakanath, Muratahan Aykol, Hanmei Tang, Iek-heng Chu, Tess Smidt, Brandon Bocklund, Matthew Horton, et al. Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows.C...

2017
[184]

Jobflow: Computational workflows made simple.Journal of Open Source Software, 9(93):5995, 2024

Andrew S Rosen, Max Gallant, Janine George, Janosh Riebesell, Hrushikesh Sahasrabuddhe, Jimmy-Xuan Shen, Mingjian Wen, Matthew L Evans, Guido Petretto, David Waroquiers, et al. Jobflow: Computational workflows made simple.Journal of Open Source Software, 9(93):5995, 2024

2024
[185]

Emmet Toolkit.https://materialsproject.github.io/emmet/, 2025

The Materials Project. Emmet Toolkit.https://materialsproject.github.io/emmet/, 2025

2025
[186]

Autogen tool.https://github.com/microsoft/autogen, 2025

Microsoft. Autogen tool.https://github.com/microsoft/autogen, 2025

2025
[187]

Crewai tool.https://github.com/crewAIInc/crewAI, 2025

CrewAI. Crewai tool.https://github.com/crewAIInc/crewAI, 2025

2025
[188]

Llamaindex tool.https://github.com/run-llama/llama_index, 2025

LlamaIndex. Llamaindex tool.https://github.com/run-llama/llama_index, 2025. 31

2025

Pith tools

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

[1] [1]

Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025

Edward O Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R Smith, and Alessandro Curioni. Foundation models for materials discovery–current state and future directions.npj Computational Materials, 11(1):61, 2025

2025

[2] [2]

Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024

Zhenzhong Wang, Haowei Hua, Wanyu Lin, Ming Yang, and Kay Chen Tan. Crystalline material discovery in the era of artificial intelligence.arXiv preprint arXiv:2408.08044, 2024

arXiv 2024

[3] [3]

Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024

Xiao-Qi Han, Xin-De Wang, Meng-Yuan Xu, Zhen Feng, Bo-Wen Yao, Peng-Jie Guo, Ze-Feng Gao, and Zhong-Yi Lu. Ai-driven inverse design of materials: Past, present and future.Chinese Physics Letters, 2024

2024

[4] [4]

A review of large language models and autonomous agents in chemistry.Chemical Science, 2025

Mayk Caldas Ramos, Christopher J Collison, and Andrew D White. A review of large language models and autonomous agents in chemistry.Chemical Science, 2025

2025

[5] [5]

A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022

Piyush Karande, Brian Gallagher, and Thomas Yong-Jin Han. A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls.Chemistry of Materials, 34(17): 7650–7665, 2022

2022

[6] [6]

Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023

Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, and Md Israfil Hossain Rimon. Scope of machine learning in materials research—a review.Applied Surface Science Advances, 18:100523, 2023

2023

[7] [7]

Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186, 2019

2019

[8] [8]

Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

2019