Predicting polarizabilities of silicon clusters using local chemical environments

Gaussian Process Regression for Materials and Molecules

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C$_{60}$

Active Learning Training Strategy for Predicting O Adsorption Free Energy on Perovskite Catalysts using Inexpensive Catalyst Features

Physics-inspired structural representations for molecules and materials

Nested sampling for materials

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE