Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials

TL;DR

This paper develops machine learning interaction potentials trained on DFT data to accurately predict bulk and surface properties of lithium metal at large scales, overcoming experimental and computational limitations.

Contribution

It introduces MLIPs that achieve high accuracy across diverse properties of lithium metal, enabling large-scale simulations beyond traditional methods.

Findings

MLIPs accurately reproduce experimental and ab-initio results

Identifies a Bell-Evans-Polanyi relation for lithium surfaces

Predicts properties inaccessible to DFT alone

Abstract

The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties and ab-initio calculations are too costly. In this work, we train Machine Learning Interaction Potentials (MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab-initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants and various surface properties inaccessible using DFT. We establish that there exists a Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.