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.

使用机器学习相互作用势 (M-LIP) 对密度泛函理论 (DFT) 数据进行预测，以实现对热力学性质、表面性质等多个方面的准确预测。研究部分关注锂金属电池和电解液的问题。