Machine-learning of atomic-scale properties based on physical principles

Machine Learning a General-Purpose Interatomic Potential for Silicon

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

Preconditioners for the geometry optimisation and saddle point search of molecular systems.

Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.

Equation of state of fluid methane from first principles with machine learning potentials

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

Bayesian inference of the spatial distributions of material properties