Improving Fidelity and Transferability of Machine-Learned Reactive Interatomic Models Through Active Learning

Force fields of machine-learned (ML) topography are ideal for describing complex phenomena including condensed phase chemistry, but parameterization is often challenging due to the proclivity for overfitting exhibited by high-flexibility models. Active learning provides an alternative route to robust ML model development, however there is no “one-size-fits” all solution. In this work, we present the Chebyshev Interaction Model for Efficient Simulation (ChIMES), a ML force field targeting chemistry in condensed phase systems. ChIMES models are comprised of linear combinations of Chebyshev polynomials explicitly describing many-body interactions and thus can also exhibit overfitting. We discuss development of a ChIMES active learning scheme leveraging physical intuition and Shannon information theory for systematic improvements in fidelity and transferability of resulting models.

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