Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

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.

arXiv commentimproved discussion, in particular in Section IV.B

References in corpus (17)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Lithium Diffusion in Graphitic Carbon
• Comparing molecules and solids across structural and alchemical space
• Active learning of linearly parametrized interatomic potentials
• Machine-learning based interatomic potential for amorphous carbon
• Feasibility of Lithium Storage on Graphene and Its Derivatives
• Nucleation mechanism for the direct graphite-to-diamond phase transition
• A neural network interatomic potential for the phase change material GeTe
• A learning scheme to predict atomic forces and accelerate materials simulations
• Density functional study of alkali metal atoms and monolayers on graphite (0001)
• First-principles interatomic potentials for ten elemental metals via compressed sensing
• Stratified construction of neural network based interatomic models for multicomponent materials
• Microscopic origins of the anomalous melting behaviour of high-pressure sodium
• Structural Simplicity as a Restraint on the Structure of Amorphous Silicon

Cited by in corpus (24)

• Machine learning for electronically excited states of molecules
• An Accurate and Transferable Machine Learning Potential for Carbon
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
• Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
• Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
• Machine Learning for Predicting Thermal Transport Properties of Solids
• Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
• Machine learning and excited-state molecular dynamics
• Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations
• Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
• Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space
• DFTB modelling of lithium intercalated graphite with machine-learned repulsive potential
• Phase stability of Au-Li binary systems studied using neural network potential
• Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
• Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential
• Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries
• Machine-learning of atomic-scale properties based on physical principles
• Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
• Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces from Modified High Dimensional Neural Networks for Applications in Energy Storage
• Fast proper orthogonal descriptors for many-body interatomic potentials
• Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
• Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C