Automated discovery of a robust interatomic potential for aluminum

Justin S. Smith (), Benjamin Nebgen (), Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin and Kipton Barros ()
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Justin S. Smith: Los Alamos National Laboratory
Benjamin Nebgen: Los Alamos National Laboratory
Nithin Mathew: Los Alamos National Laboratory
Jie Chen: Los Alamos National Laboratory
Nicholas Lubbers: Los Alamos National Laboratory
Leonid Burakovsky: Los Alamos National Laboratory
Sergei Tretiak: Los Alamos National Laboratory
Hai Ah Nam: Los Alamos National Laboratory
Timothy Germann: Los Alamos National Laboratory
Saryu Fensin: Los Alamos National Laboratory
Kipton Barros: Los Alamos National Laboratory

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21376-0

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DOI: 10.1038/s41467-021-21376-0

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