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

Midwest Integrated Center for Computational Materials - Publications

Ambipolar diffusion of ions and electrons as well as the chemical

Unified quantum theory of electrochemical kinetics by coupled ion

Machine learning accelerates quantum mechanics predictions of

Improving Hydride Conductivity in Layered Perovskites via Crystal

Hibbitts Group Publications

Published Papers – Computational Chemical Science Center

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems - ScienceDirect

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

Evidence for a Solid-Electrolyte Inductive Effect in the Superionic Conductor Li10Ge1-xSnxP2S12. - Abstract - Europe PMC

PDF) Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys