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The properties of lithium metal are key parameters in the design of lithiumion and lithium metal batteries. They are difficult to probe experimentally dueto the high reactivity and low melting point of lithium as well as themicroscopic scales at which lithium exists in batteries where it is found tohave enhanced strength, with implications for dendrite suppression strategies.Computationally, there is a lack of empirical potentials that are consistentlyquantitatively accurate across all properties and ab-initio calculations aretoo costly. In this work, we train Machine Learning Interaction Potentials(MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy inreproducing experimental and ab-initio results across a wide range ofsimulations at large length and time scales. We accurately predictthermodynamic properties, phonon spectra, temperature dependence of elasticconstants and various surface properties inaccessible using DFT. We establishthat there exists a Bell-Evans-Polanyi relation correlating the self-adsorptionenergy and the minimum surface diffusion barrier for high Miller index facets.

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