Predicting ion diffusion from the shape of potential energy landscapes, Materials Chemistry, ChemRxiv

We present an efficient method to compute diffusion coefficients of multi-particle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion in crystalline inorganic solids, predicting Li-ion diffusion coefficients within one order of magnitude of molecular dynamics simulations at the same level of theory while being several orders of magnitude faster. The speed and transferability of our workflow make it well suited for extensive and efficient screening studies of crystalline solid-state ion conductor candidates and promise to serve as a platform for diffusion prediction even up to density functional level of theory.

Midwest Integrated Center for Computational Materials - Publications

Machine-learned potentials for next-generation matter simulations

PDF) Ion mobility in crystalline battery materials

Recent advances and applications of machine learning in solid-state materials science

Predicting Ion Diffusion from the Shape of Potential Energy

OpenKIM · SNAP ZuoChenLi 2019 Ge MO_183216355174_000

Midwest Integrated Center for Computational Materials - Publications

Classical and reactive molecular dynamics: Principles and

Understanding MOF Flexibility: An Analysis Focused on Pillared