The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

This article gives a perspective on the progress of AI tools in computational chemistry
through the lens of the author's decade-long contributions put in the wider context of the …

Abstract Machine learning (ML) based interatomic potentials have transformed the field of
atomistic materials modelling. However, ML potentials depend critically on the quality and …

The development of machine learning models has led to an abundance of datasets
containing quantum mechanical (QM) calculations for molecular and material systems …

Effective transfer learning for molecular property prediction has shown considerable strength
in addressing insufficient labeled molecules. Many existing methods either disregard the …

Machine-learned potentials (MLPs) trained on ab initio data combine the computational
efficiency of classical interatomic potentials with the accuracy and generality of the first …

Data-driven interatomic potentials (IPs) trained on large collections of first principles
calculations are rapidly becoming essential tools in the fields of computational materials …

Interatomic potentials learned using machine learning methods have been successfully
applied to atomistic simulations. However, accurate models require large training datasets …