Towards the holistic design of alloys with large language models

Pei, Zongrui; Yin, Junqi; Neugebauer, Jörg; Jain, Anubhav

doi:10.1038/s41578-024-00726-6

Towards the holistic design of alloys with large language models

Journal Article · Thu Sep 12 00:00:00 EDT 2024 · Nature Reviews. Materials

DOI:https://doi.org/10.1038/s41578-024-00726-6· OSTI ID:2472681

^[1]; ^[2]; Neugebauer, Jörg ^[3]; ^[4]

New York Univ. (NYU), NY (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Max-Planck-Institut für Eisenforschung (Germany)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Large language models are very effective at solving general tasks, but can also be useful in materials design and extracting and using information from the scientific literature and unstructured corpora. For instance, in the domain of alloy design and manufacturing, they can expedite the materials design process and enable the inclusion of holistic criteria.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2472681

Journal Information:: Nature Reviews. Materials, Journal Name: Nature Reviews. Materials Vol. 9; ISSN 2058-8437

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (8)

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An overview of modeling the stacking faults in lightweight and high-entropy alloys: Theory and application Pei, Zongrui Materials Science and Engineering: A, Vol. 737 https://doi.org/10.1016/j.msea.2018.09.028	journal	November 2018
Machine learning for high-entropy alloys: Progress, challenges and opportunities Liu, Xianglin; Zhang, Jiaxin; Pei, Zongrui Progress in Materials Science, Vol. 131 https://doi.org/10.1016/j.pmatsci.2022.101018	journal	January 2023
Toward the design of ultrahigh-entropy alloys via mining six million texts Pei, Zongrui; Yin, Junqi; Liaw, Peter K. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-022-35766-5	journal	January 2023
Unsupervised word embeddings capture latent knowledge from materials science literature Tshitoyan, Vahe; Dagdelen, John; Weston, Leigh Nature, Vol. 571, Issue 7763 https://doi.org/10.1038/s41586-019-1335-8	journal	July 2019
Data-driven materials research enabled by natural language processing and information extraction Olivetti, Elsa A.; Cole, Jacqueline M.; Kim, Edward Applied Physics Reviews, Vol. 7, Issue 4 https://doi.org/10.1063/5.0021106	journal	December 2020
Roadmap on data-centric materials science Bauer, Stefan; Benner, Peter; Bereau, Tristan Modelling and Simulation in Materials Science and Engineering, Vol. 32, Issue 6 https://doi.org/10.1088/1361-651X/ad4d0d	journal	July 2024
Llama 2: Open Foundation and Fine-Tuned Chat Models Touvron, Hugo; Martin, Louis; Stone, Kevin arXiv https://doi.org/10.48550/arXiv.2307.09288	preprint	January 2023

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