wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

Machine-learned acceleration for molecular dynamics in CASTEP

Structural Dynamics Descriptors for Metal Halide Perovskites

Tensor-Reduced Atomic Density Representations

Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science

Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule

Machine Learning Force Field for Molecular Liquids: EC/EMC Binary Solvent

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration

Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments