The evolution of machine learning potentials for molecules, reactions and materials
Junfan Xia, Yaolong Zhang, Bin Jiang
Abstract
data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs.
Topics & Concepts
Computer scienceMoleculeNanotechnologyCognitive scienceChemistryMaterials sciencePsychologyOrganic chemistryMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics