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.

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Machine learning accelerates quantum mechanics predictions of

OpenKIM · SNAP ZuoChenLi 2019 Ge MO_183216355174_000

Published Papers – Computational Chemical Science Center

PDF) Nonadiabatic dynamics in multidimensional complex potential

PDF) Acid-Base Chemistry of a Model IrO2 Catalytic Interface

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

IJMS, Free Full-Text

Computational insights into ionic conductivity of transition metal

Understanding MOF Flexibility: An Analysis Focused on Pillared

Quantum dynamical effects of vibrational strong coupling in chemical reactivity

Machine learning accelerates quantum mechanics predictions of