Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

TL;DR

This paper develops machine-learning based Gaussian approximation potentials to model lithium intercalation in carbon nanostructures, enabling detailed atomistic simulations relevant for battery materials.

Contribution

It introduces a method to model guest atoms in host frameworks using GAP, combining DFT data with explicit pair potentials for improved accuracy.

Findings

GAP models accurately reproduce lithium interactions in carbon structures.

Explicit pair potentials enhance the modeling of Li--Li interactions.

Proof-of-concept for machine-learning potentials in battery material simulations.

Abstract

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density-functional theory (DFT) data. Rather than treating the full Li--C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li--Li interactions, to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials, and in the longer run is promising for carrying out detailed atomistic studies of battery materials.