Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials

TL;DR

This paper introduces a symmetry- and gradient-enhanced Gaussian Process Regression algorithm with active learning, enabling efficient and accurate determination of molecular potential energy surfaces crucial for modeling gas transport in porous materials.

Contribution

It presents a novel, cost-effective algorithm combining symmetry, gradient information, and active learning for potential energy surface prediction in porous materials.

Findings

High accuracy in gas sieving scenarios on N-functionalized graphene

Effective reduction in single point evaluations

Successful modeling of CH4 and N2 interactions

Abstract

The theoretical investigation of gas adsorption, storage, separation, diffusion and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In this article, a new algorithm is presented, specifically developed for gas transport phenomena, which allows for a highly cost-effective determination of molecular potential energy surfaces. It is based on a symmetry-enhanced version of Gaussian Process Regression with embedded gradient information and employs an active learning strategy to keep the number of single point evaluations as low as possible. The performance of the algorithm is tested for a selection of gas sieving scenarios on porous, N-functionalized graphene and for the intermolecular interaction of CH$_4$ and N$_2$.