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Creating Gaussian Process Regression Models for Molecular Simulations Using Adaptive Sampling
preprintsubmitted on 22.06.2020, 12:04 and posted on 24.06.2020, 10:46 by Matthew J. Burn, Paul Popelier
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.