MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC B , the papers address ML applications in protein engineering, materials design, novel methods, and properties of liquids. Several contributions specifically address the novel uses of ML as a methodological improvement or innovation. − Another common thread among the papers is the use of ML to study issues related to molecular conformers or other structure–function relationships. − A number of articles demonstrate that ML has continued to grow as an important tool for the study of complex liquids and their properties. − Finally, papers in JPC B also show recent advances in the application of ML to the study of peptides, proteins, and their properties. − …”