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.

IJMS, Free Full-Text

Machine learning accelerates quantum mechanics predictions of

Energies, Free Full-Text

Computational insights into ionic conductivity of transition metal

Nanoparticle synthesis assisted by machine learning

Anion-Exchange Membrane Water Electrolyzers

The Many-Body Expansion for Aqueous Systems Revisited: II. Alkali

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

OpenKIM · SNAP ZuoChenLi 2019 Li MO_732106099012_000 MO_732106099012 · Interatomic Potentials and Force Fields

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

Quantum dynamical effects of vibrational strong coupling in chemical reactivity

Midwest Integrated Center for Computational Materials - Publications

Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence