Toward Gaussian Process Regression Modeling of a Urea Force Field.

ABSTRACT: FFLUX is a next-generation, machine-learnt force field built on three cornerstones: quantum chemical topology, Gaussian process regression, and (high-rank) multipolar electrostatics. It is capable of performing molecular dynamics with near-quantum accuracy at a lower computational cost than standard ab initio molecular dynamics. Previous work with FFLUX was concerned with water and formamide. In this study, we go one step further and challenge FFLUX to model urea, a larger and more flexible system. In result, we have trained urea models at the B3LYP/aug-cc-pVTZ level of theory, with a mean absolute error of 0.4 kJ mol^-1 and a maximum prediction error below 7.0 kJ mol^-1. To test their performance in molecular dynamics simulations, two sets of FFLUX geometry opti

SUBMITTER: Bukowy T 

PROVIDER: S-EPMC11457224 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature