Extending Atomic Decomposition and Many-Body Representation with a Chemistry-Motivated Approach to Machine Learning Potentials

Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials

Most widely used machine learning potentials for condensed-phase applications rely on many-body permutationally invariant polynomial or atom-centered neural networks. However, these approaches face challenges in achieving chemical interpretability in atomistic energy decomposition and fully matching the computational efficiency of traditional force fields. Here we present a method that combines aspects of both approaches and balances accuracy an…

Machine Learning Potentials Advance Solvation Modelling For Complex Systems

Machine-learned potentials (MLPs) represent a developing computational approach to modelling solvation – the interaction of solvent molecules with solutes – by offering a balance between the accuracy of first-principles methods and the computational efficiency required for simulating complex, large-scale systems, with current research focused on improving their transferability and physical grounding for wider application in atomistic modelling o…