Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
This work addresses the difficulty in computationally predicting lithium metal properties for battery applications, offering a more accurate and scalable alternative to existing methods, though it is incremental as it builds on MLIPs for a specific domain.
The researchers tackled the challenge of accurately modeling lithium metal properties for battery design by training Machine Learning Interaction Potentials (MLIPs) on Density Functional Theory data, achieving state-of-the-art accuracy in predicting thermodynamic, elastic, and surface properties at large scales, including establishing a Bell-Evans-Polanyi relation for surface diffusion barriers.
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.