ReviewDigital discovery2023
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.
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Review in Digital discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47citing papers in PubMed
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Who cites it
47 citing papers in PubMed.
- Article
- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Materials Databases: Foundations of Modern Digital Materials.Precision chemistry · 2026Review
- TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.Chemical science · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- FePTP: A text-mined dataset of transformation pathways among iron-containing phases.Scientific data · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.ACS central science · 2026Review
- Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology.Discover artificial intelligence · 2026Review
- Large language models as uncertainty-calibrated optimizers for experimental discovery.Nature machine intelligence · 2026Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Probing the limitations of multimodal language models for chemistry and materials research.Nature computational science · 2025Article
- 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.Machine learning: science and technology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
53 authors.
Kevin Maik JablonkaLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0003-4894-4660
Qianxiang AiDepartment of Chemical Engineering, Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA.ORCID https://orcid.org/0000-0002-5487-2539
Alexander Al-FeghaliDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0009-0004-8377-7049
Shruti BadhwarReincarnate Inc. USA.ORCID https://orcid.org/0000-0002-3167-5348
Joshua D BocarslyYusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK.ORCID https://orcid.org/0000-0002-7523-152X
Andres M BranLaboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland.ORCID https://orcid.org/0000-0002-4432-3667
Stefan BringuierIndependent Researcher San Diego CA USA.ORCID https://orcid.org/0000-0001-6753-1437
L Catherine BrinsonMechanical Engineering and Materials Science, Duke University USA.ORCID https://orcid.org/0000-0003-2551-1563
Kamal ChoudharyMaterial Measurement Laboratory, National Institute of Standards and Technology Maryland 20899 USA.ORCID https://orcid.org/0000-0001-9737-8074
Defne CirciMechanical Engineering and Materials Science, Duke University USA.ORCID https://orcid.org/0000-0002-5761-0198
Sam CoxDepartment of Chemical Engineering, University of Rochester USA.ORCID https://orcid.org/0000-0002-4441-9327
Wibe A de JongApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA.ORCID https://orcid.org/0000-0002-7114-8315
Matthew L EvansInstitut de la Matière Condensée et des Nanosciences (IMCN), UCLouvain Chemin des Étoiles 8 Louvain-la-Neuve 1348 Belgium.ORCID https://orcid.org/0000-0002-1182-9098
Nicolas GastelluDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-4052-076X
Jerome GenzlingDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0009-0007-4728-1478
María Victoria GilInstituto de Ciencia y Tecnología del Carbono (INCAR), CSIC Francisco Pintado Fe 26 33011 Oviedo Spain.ORCID https://orcid.org/0000-0002-2258-3011
Ankur K GuptaApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA.ORCID https://orcid.org/0000-0002-3128-9535
Zhi HongDepartment of Computer Science, University of Chicago Chicago Illinois 60637 USA.ORCID https://orcid.org/0000-0002-5250-1347
Alishba ImranComputer Science, University of California Berkeley CA 94704 USA.
Sabine KruschwitzBundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin Germany.ORCID https://orcid.org/0000-0002-6296-4417
Anne LabarreDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0003-4939-3928
Jakub LálaFrancis Crick Institute 1 Midland Rd London NW1 1AT UK.ORCID https://orcid.org/0000-0002-5424-5260
Tao LiuDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-1082-5570
Steven MaDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0009-0006-9448-7332
Sauradeep MajumdarLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0002-2095-3082
Garrett W MerzAmerican Family Insurance Data Science Institute, University of Wisconsin-Madison Madison WI 53706 USA.ORCID https://orcid.org/0000-0003-4737-3931
Nicolas MoitessierDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0001-6933-2079
Elias MoubarakLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0001-8271-6800
Beatriz MouriñoLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0003-1670-3985
Brenden PelkieDepartment of Chemical Engineering, University of Washington Seattle WA 98105 USA.ORCID https://orcid.org/0000-0001-7638-6366
Michael PielerOpenBioML.org UK.ORCID https://orcid.org/0000-0001-9186-7045
Mayk Caldas RamosDepartment of Chemical Engineering, University of Rochester USA.ORCID https://orcid.org/0000-0001-5336-2847
Bojana RankovićLaboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland.ORCID https://orcid.org/0000-0002-1476-6686
Samuel G RodriquesFrancis Crick Institute 1 Midland Rd London NW1 1AT UK.ORCID https://orcid.org/0000-0002-2509-0861
Jacob N SandersDepartment of Chemistry and Biochemistry, University of California Los Angeles CA 90095 USA.ORCID https://orcid.org/0000-0002-2196-4234
Philippe SchwallerLaboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland.ORCID https://orcid.org/0000-0003-3046-6576
Marcus SchwartingDepartment of Computer Science, University of Chicago Chicago IL 60490 USA.
Jiale ShiDepartment of Chemical Engineering, Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA.ORCID https://orcid.org/0000-0002-5447-3925
Berend SmitLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0003-4653-8562
Ben E SmithYusuf Hamied Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK.ORCID https://orcid.org/0000-0001-9673-2449
Joren Van HerckLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0009-0005-5108-5061
Christoph VölkerBundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin Germany.ORCID https://orcid.org/0000-0002-0985-0074
Logan WardData Science and Learning Division, Argonne National Lab USA.ORCID https://orcid.org/0000-0002-1323-5939
Sean WarrenDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-3670-0354
Benjamin WeiserDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-3770-7224
Sylvester ZhangDepartment of Chemistry, McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-4793-1131
Xiaoqi ZhangLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL) Sion Valais Switzerland mail@kjablonka.com.ORCID https://orcid.org/0000-0002-6507-6490
Ghezal Ahmad ZiaBundesanstalt für Materialforschung und -prüfung Unter den Eichen 87 12205 Berlin Germany.ORCID https://orcid.org/0000-0002-9082-9423
Aristana ScourtasGlobus, University of Chicago, Data Science and Learning Division, Argonne National Lab USA blaiszik@uchicago.edu.ORCID https://orcid.org/0000-0002-3917-605X
K J SchmidtGlobus, University of Chicago, Data Science and Learning Division, Argonne National Lab USA blaiszik@uchicago.edu.ORCID https://orcid.org/0000-0002-9373-0058
Ian FosterDepartment of Computer Science, University of Chicago, Data Science and Learning Division, Argonne National Lab USA.ORCID https://orcid.org/0000-0003-2129-5269
Andrew D WhiteDepartment of Chemical Engineering, University of Rochester USA.ORCID https://orcid.org/0000-0002-6647-3965
Ben BlaiszikGlobus, University of Chicago, Data Science and Learning Division, Argonne National Lab USA blaiszik@uchicago.edu.ORCID https://orcid.org/0000-0002-5326-4902
Funding
Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous LaboratoryU18TR004149 · NCATS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI COLEY, CONNOR WILSON · 2022 to 2023
$1.1MLearning to learn in structural biology with deep neural networksR35GM137966 · NIGMS · UNIVERSITY OF ROCHESTER · PI WHITE, ANDREW DAVID · 2020 to 2023
$1.0MNCATS NIH HHS U18 TR004149NIGMS NIH HHS R35 GM137966Wellcome Trust
Abstract
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
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