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Title: 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:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [2];  [4]; ORCiD logo [2];  [5]; ORCiD logo [2];  [4]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Florida, Gainesville, FL (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Jackson State Univ., MS (United States)
  4. Univ. of Florida, Gainesville, FL (United States)
  5. Univ. of North Carolina, Chapel Hill, NC (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (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. 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, and Roitberg, Adrian E. Mon . "Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning". United States. doi: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 = {2019},
month = {7}
}

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Cited by: 3 works
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    Works referencing / citing this record:

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    journal, August 2017


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    journal, March 2011

    • Grimme, Stefan
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    A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments
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    • Sellers, Benjamin D.; James, Natalie C.; Gobbi, Alberto
    • Journal of Chemical Information and Modeling, Vol. 57, Issue 6
    • DOI: 10.1021/acs.jcim.6b00614

    Protein–Ligand Scoring with Convolutional Neural Networks
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    • Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa
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    Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations
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    • Bleiziffer, Patrick; Schaller, Kay; Riniker, Sereina
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    • DOI: 10.1038/s41467-018-06169-2

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    A simple and efficient CCSD(T)-F12 approximation
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    • Adler, Thomas B.; Knizia, Gerald; Werner, Hans-Joachim
    • The Journal of Chemical Physics, Vol. 127, Issue 22
    • DOI: 10.1063/1.2817618

    Systematic optimization of long-range corrected hybrid density functionals
    journal, February 2008

    • Chai, Jeng-Da; Head-Gordon, Martin
    • The Journal of Chemical Physics, Vol. 128, Issue 8
    • DOI: 10.1063/1.2834918

    A full coupled‐cluster singles and doubles model: The inclusion of disconnected triples
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    • Purvis, George D.; Bartlett, Rodney J.
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    • Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
    • Science Advances, Vol. 3, Issue 5
    • DOI: 10.1126/sciadv.1603015

    Machine learning unifies the modeling of materials and molecules
    journal, December 2017

    • Bartók, Albert P.; De, Sandip; Poelking, Carl
    • Science Advances, Vol. 3, Issue 12
    • DOI: 10.1126/sciadv.1701816

    The Protein-Folding Problem, 50 Years On
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    Amplify scientific discovery with artificial intelligence
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    Machine learning: Trends, perspectives, and prospects
    journal, July 2015


    Predicting reaction performance in C–N cross-coupling using machine learning
    journal, February 2018


    Biomolecular Simulation: A Computational Microscope for Molecular Biology
    journal, June 2012


    Inferring multi-target QSAR models with taxonomy-based multi-task learning
    journal, July 2013

    • Rosenbaum, Lars; Dörr, Alexander; Bauer, Matthias R.
    • Journal of Cheminformatics, Vol. 5, Issue 1
    • DOI: 10.1186/1758-2946-5-33