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.

Quantum dynamical effects of vibrational strong coupling in

/cms/10.1021/acs.chemrev.1c00981/asset

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Predicting ion diffusion from the shape of potential energy

Energies, Free Full-Text

Reversible assembly of nanoparticles: theory, strategies and

Understanding MOF Flexibility: An Analysis Focused on Pillared

Pushing the boundaries of lithium battery research with atomistic modelling on different scales - IOPscience

Why Do Liquids Mix? The Mixing of Protic Ionic Liquids Sharing the Same Cation Is Apparently Driven by Enthalpy, Not Entropy

Unified quantum theory of electrochemical kinetics by coupled ion

OpenKIM · SNAP ZuoChenLi 2019quadratic Li MO_041269750353_000

Anion-Exchange Membrane Water Electrolyzers