Creating Gaussian Process Regression Models for Molecular Simulations Using Adaptive Sampling

FFLUX is a new force field that combines the accuracy of quantum mechanics with the speed of force

fields, without any link to the architecture of classical force fields. This force field is atom‐focused and

adopts the parameter‐free topological atom from Quantum Chemical Topology (QCT). FFLUX uses

Gaussian Process Regression (GPR) (aka kriging) models to make predicƟons of atomic properties, which

in this work are atomic energies according to QCT’s InteracƟng Quantum Atom (IQA) approach. Here

we report the adaptive sampling technique Maximum Expected Prediction Error (MEPE) to create data‐

compact, efficient and accurate kriging models (sub kJ mol‐1 for water, ammonia, methane and

methanol, and sub kcal mol‐1 for N‐methylacetamide (NMA)). The models cope with large molecular

distortions and are ready for use in molecular simulation. A brand new press‐one‐buƩon Python

pipeline, called ICHOR, carries out the training.

Abstract