Description of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Models

Abstract

A new type of model, FFLUX, to describe the interaction between atoms has been developed as an alternative to traditional force fields. FFLUX models are constructed from applying the kriging machine learning method to the topological energy partitioning method, Interacting Quantum Atoms (IQA). The effect of varying parameters in the construction of the FFLUX models is analyzed, with the most dominant effects found to be the structure of the molecule and the number of conformations used to build the model. Using these models the optimization of a variety of small organic molecules is performed, with sub kJ mol-1 accuracy in the energy of the optimized molecules. The FFLUX models are also evaluated in terms of their performance in describing the potential energy surfaces (PESs) associated with specific degrees of freedoms within molecules. While the accurate description of PESs presents greater challenges than individual minima, FFLUX models are able to achieve errors of <2.5 kJ mol-1 across the full C-C-C-C dihedral PES of n-butane, indicating the future possibilities of the technique.