Achieving Quantum-Accurate Condensed-Phase Reactive Simulations through Machine-Learned Force Fields

Understanding chemistry at extreme conditions is crucial in fields including geochemistry, astrobiology, and alternative energy. First principles (quantum-mechanical) methods can provide valuable microscopic insights into such systems while circumventing the risks of physical experiments, however the time and length scales associated with chemistry at high temperature and pressure (i.e. ns and μm, respectively) largely preclude extension of such models to molecular dynamics. In this work, we discuss development of ChIMES, a generalized n-body force field comprised of linear combinations of Chebyshev polynomials. ChIMES models are machine-learned to selected configurations from short Density Functional Theory (DFT) molecular dynamics simulations and are refined through active learning. ChIMES models are found to retain much of the accuracy of DFT at a fraction of the cost and exhibit linear size scalability.

This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-751889