Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
Abstract
A machine learning framework for molecular dynamics simulations using multipole featurization enables efficient calculations with multiple chemical elements by reducing density functional theory calls.
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme.
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