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Cited by 126Machine Learning in Materials ScienceComputational Drug Discovery MethodsAdvanced Chemical Physics Studies

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Neural scaling of deep chemical models

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Frey, Nathan C., Soklaski, Ryan, Axelrod, Simon, Samsi, Siddharth, Gómez-Bombarelli, Rafael, Coley, Connor W., Gadepally, Vijay·2023·Nature Machine Intelligence
DOI 10.1038/s42256-023-00740-3

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Abstract

Massive scale, in terms of both data availability and computation, enables important breakthroughs in key application areas of deep learning such as natural language processing and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of physical priors in scientific domains makes the strategies and benefits of scaling uncertain. Here we investigate neural-scaling behaviour in large chemical models by varying model and dataset sizes over many orders of magnitude, studying models with over one billion parameters, pre-trained on datasets of up to ten million datapoints. We consider large language models for generative chemistry and graph neural networks for machine-learned interatomic potentials. We investigate the interplay between physical priors and scale and discover empirical neural-scaling relations for language models in chemistry with a scaling exponent of 0.17 for the largest dataset size considered, and a scaling exponent of 0.26 for equivariant graph neural network interatomic potentials.

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Metadata

Authors
Frey, N. C., Soklaski, R., Axelrod, S., Samsi, S., Gómez-Bombarelli, R., Coley, C. W., & Gadepally, V.
Affiliations
  • MIT Lincoln Laboratory (US)
  • Harvard University (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Frey, Nathan C.
ORCID
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2023-10-23
Volume
5
Issue
11
Pages
1297-1305
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-023-00740-3
OpenAlex
W4387874094
Cited by
125
References
48
Citations by year
2026: 41 · 2025: 43 · 2024: 36 · 2023: 4
FWCI
9.16
Type
journal article
Language
en
Open access
Licence
Locations
Nature Machine Intelligence (publishedVersion, cc-by)
Funders
United States Department of Defense | U.S. Air Force (FA8702-15-D-0001)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
physical priors, generative chemistry, large language models, graph neural networks, interatomic potentials, equivariant graph neural networks, scaling exponents, model size, dataset size

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