Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak (), Olexandr Isayev () and Adrian E. Roitberg ()
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Justin S. Smith: University of Florida
Benjamin T. Nebgen: Los Alamos National Laboratory
Roman Zubatyuk: Los Alamos National Laboratory
Nicholas Lubbers: Los Alamos National Laboratory
Christian Devereux: University of Florida
Kipton Barros: Los Alamos National Laboratory
Sergei Tretiak: Los Alamos National Laboratory
Olexandr Isayev: University of North Carolina at Chapel Hill
Adrian E. Roitberg: University of Florida

Nature Communications, 2019, vol. 10, issue 1, 1-8

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-10827-4

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