Assessment of chemistry knowledge in large language models that generate code-Reference-Cited by-同舟云学术

Assessment of chemistry knowledge in large language models that generate code

Published:2023 Issue:2 Volume:2 Page:368-376
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

White Andrew D.¹²^ORCID,Hocky Glen M.³⁴^ORCID,Gandhi Heta A.¹^ORCID,Ansari Mehrad¹^ORCID,Cox Sam¹^ORCID,Wellawatte Geemi P.⁵^ORCID,Sasmal Subarna³^ORCID,Yang Ziyue¹^ORCID,Liu Kangxin³^ORCID,Singh Yuvraj³^ORCID,Peña Ccoa Willmor J.³^ORCID

Affiliation:

1. Department of Chemical Engineering, University of Rochester, USA

2. Vial Health Technology, Inc., USA

3. Department of Chemistry, New York University, USA

4. Simons Center for Computational Physical Chemistry, New York University, USA

5. Department of Chemistry, University of Rochester, USA

Abstract

In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes.

Funder

National Institute of General Medical Sciences

National Science Foundation

National Institutes of Health

U.S. Department of Energy

Simons Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00087C

Reference51 articles.

1. A.Vaswani , N.Shazeer , N.Parmar , J.Uszkoreit , L.Jones , A. N.Gomez , Ł.Kaiser , I.Polosukhin , Attention is all you need , Adv. Neural Inf. Process. Syst. , 2017 , vol. 30

2. J.Devlin , M.-W.Chang , K.Lee and K.Toutanova , Bert: pre-training of deep bidirectional transformers for language understanding , arXiv , 2018 , preprint, arXiv:1810.04805, 10.48550/arXiv.1810.04805

3. A.Srivastava , A.Rastogi , A.Rao , A. A. M.Shoeb , A.Abid , A.Fisch , A. R.Brown , A.Santoro , A.Gupta , A.Garriga-Alonso , et al., Beyond the imitation game: quantifying and extrapolating the capabilities of language models , arXiv , 2022 , preprint, arXiv:2206.04615, 10.48550/arXiv.2206.04615

4. Natural language processing models that automate programming will transform chemistry research and teaching

Cited by 39 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large Language Models as Molecular Design Engines;Journal of Chemical Information and Modeling;2024-09-04

2. Coding with AI in the Physical Chemistry Laboratory;Journal of Chemical Education;2024-08-27

3. Quantum chemical package Jaguar: A survey of recent developments and unique features;The Journal of Chemical Physics;2024-08-02

4. Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI⁺ era;Materials Genome Engineering Advances;2024-07-16

5. Large Language Models for Inorganic Synthesis Predictions;Journal of the American Chemical Society;2024-07-11