Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Data-driven learning and prediction of inorganic crystal structures.

Towards an atomistic understanding of disordered carbon electrode materials

Machine Learning Unifies the Modelling of Materials and Molecules

Machine learning unifies the modeling of materials and molecules.

Constant-pressure nested sampling with atomistic dynamics (vol 96, 043311, 2017)

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Constant-pressure nested sampling with atomistic dynamics