Neural scaling of deep chemical models
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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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- 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
- Published
- 2023-10-23
- Volume
- 5
- Issue
- 11
- Pages
- 1297-1305
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4387874094
- Cited by
- 125
- References
- 48
- Citations by year
- 2026: 41 · 2025: 43 · 2024: 36 · 2023: 4 · 2022: 1
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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