Nongnuch Artrith

A practical guide to machine learning interatomic potentials – Status and future

Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes

Highly Antioxidative Lithium Salt Enables High-Voltage Ether Electrolyte for Lithium Metal Battery

ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

Mixed hydride-electronic conductivity in Rb2CaH4 and Cs2CaH4

Ultrafast X-ray imaging of the light-induced phase transition in VO2

Artificial Intelligence-Aided Mapping of the Structure–Composition–Conductivity Relationships of Glass–Ceramic Lithium Thiophosphate Electrolytes

Understanding the Onset of Surface Degradation in LiNiO₂ Cathodes

Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading

AENET–LAMMPS and AENET–TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2

Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation

Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials