By Fernanda Duarte, Department of Chemistry, University of Oxford, UK   Efficiently and accurately modelling chemical reactions in the condensed phase stands as one of the ‘grand challenges’ in computational chemistry. Although characterizing reaction energy pathways has become routine, the exploration of intricate reaction mechanisms remains a time-consuming and non-systematic endeavour, even for experienced computational […]

  By Volker Deringer, Department of Chemistry, University of Oxford, UK Machine learning (ML) based interatomic potential models are increasingly popular simulation tools for molecular and materials systems and hold promise for use on exascale supercomputers . ML potentials are fitted to large sets of quantum-mechanical reference data, and therefore developing high-quality datasets and automated […]