This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

Applications of machine learning techniques for materials modeling typically involve
functions that are known to be equivariant or invariant to specific symmetries. While graph …

In the field of molecular simulation for drug design, traditional molecular mechanic force
fields and quantum chemical theories have been instrumental but limited in terms of …

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the
state-of-the-art neural network models are approaching ab initio accuracy for molecular …

Neural architectures that learn potential energy surfaces from molecular data have
undergone fast improvement in recent years. A key driver of this success is the Message …

Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and
forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC …

As the sophistication of machine learning force fields (MLFF) increases to match the
complexity of extended molecules and materials, so does the need for tools to properly …