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

OpenKIM · SNAP ZuoChenLi 2019quadratic Li MO_041269750353_000

3 Measurement and Control of Molecular Quantum Systems Advancing Chemistry and Quantum Information Science: An Assessment of Research Opportunities at the Interface of Chemistry and Quantum Information Science in the United

Nanoparticle synthesis assisted by machine learning

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

Quantum dynamical effects of vibrational strong coupling in

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

Understanding MOF Flexibility: An Analysis Focused on Pillared

IJMS, Free Full-Text

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

Energies, Free Full-Text

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