Atomistic modelling plays an important role in representing the properties and behaviour of materials at the atomic scale. This innovation study aims to optimise atomistic materials modelling by adding physics-based functionality to Machine Learning Potentials (MLPs). Traditional interatomic potentials (IPs) have limitations in accurately capturing the complex dynamics of chemical bond formation and breakage, limiting their applicability in various materials science and biomolecular design tasks. MLPs provide a solution to overcome these challenges. Machine Learning … Continue reading XSCALE