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; Kipton Barros

doi:10.1038/s41467-021-21376-0

Automated discovery of a robust interatomic potential for aluminum

Nat Commun. 2021 Feb 23;12(1):1257. doi: 10.1038/s41467-021-21376-0.

Authors

Justin S Smith^{1

2}, Benjamin Nebgen³, Nithin Mathew^{4

5}, Jie Chen⁶, Nicholas Lubbers⁷, Leonid Burakovsky⁴, Sergei Tretiak⁴, Hai Ah Nam⁷, Timothy Germann⁴, Saryu Fensin⁶, Kipton Barros⁸

Affiliations

¹ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. just@lanl.gov.
² Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA. just@lanl.gov.
³ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. bnebgen@lanl.gov.
⁴ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁵ Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁶ Materials Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁷ Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁸ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. kbarros@lanl.gov.

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.