Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Abstract
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.
- Authors:
-
[1];
[2];
[2];
[2];
[2];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Florida, Gainesville, FL (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Jackson State Univ., MS (United States)
- Univ. of Florida, Gainesville, FL (United States)
- Univ. of North Carolina, Chapel Hill, NC (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1544717
- Report Number(s):
- LA-UR-18-25687
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, and Roitberg, Adrian E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. United States: N. p., 2019.
Web. doi:10.1038/s41467-019-10827-4.
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, & Roitberg, Adrian E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. United States. https://doi.org/10.1038/s41467-019-10827-4
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, and Roitberg, Adrian E. Mon .
"Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning". United States. https://doi.org/10.1038/s41467-019-10827-4. https://www.osti.gov/servlets/purl/1544717.
@article{osti_1544717,
title = {Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning},
author = {Smith, Justin Steven and Nebgen, Benjamin Tyler and Zubatiuk, Roman and Lubbers, Nicholas Edward and Devereux, Christian and Barros, Kipton Marcos and Isayev, Olexandr and Tretiak, Sergei and Roitberg, Adrian E},
abstractNote = {Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.},
doi = {10.1038/s41467-019-10827-4},
journal = {Nature Communications},
number = 1,
volume = 10,
place = {United States},
year = {Mon Jul 01 00:00:00 EDT 2019},
month = {Mon Jul 01 00:00:00 EDT 2019}
}
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- Current Genetic Medicine Reports, Vol. 7, Issue 4
Machine learning and artificial neural network accelerated computational discoveries in materials science
journal, November 2019
- Hong, Yang; Hou, Bo; Jiang, Hengle
- WIREs Computational Molecular Science, Vol. 10, Issue 3
Dataset’s chemical diversity limits the generalizability of machine learning predictions
journal, November 2019
- Glavatskikh, Marta; Leguy, Jules; Hunault, Gilles
- Journal of Cheminformatics, Vol. 11, Issue 1
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
journal, September 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Advanced Materials, Vol. 31, Issue 46
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.
journalarticle, January 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Wiley
Efficient Cysteine Conformer Search with Bayesian Optimization
preprint, January 2020
- Fang, Lincan; Makkonen, Esko; Todorovic, Milica
- arXiv
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
journal, August 2019
- Zubatyuk, Roman; Smith, Justin S.; Leszczynski, Jerzy
- Science Advances, Vol. 5, Issue 8
Transforming solid-state precipitates via excess vacancies
journal, March 2020
- Bourgeois, Laure; Zhang, Yong; Zhang, Zezhong
- Nature Communications, Vol. 11, Issue 1