Machine Learning Meets Chemical Physics

This special topic welcomes papers from all corners of chemical physics that involve the development and the application to both simulations and experiments of machine-learning techniques. These may include classification, regression, experimental design, generative models, supervised and unsupervised learning. We seek in particular work that emphasizes the interplay between machine learning and chemical physics — be it by incorporating physical principles and chemical intuition into the construction of the model, or by using machine learning to recognize new laws or general design principles — as opposed to papers that use off-the-shelf machine learning schemes to sift through data to solve one particular problem.

Guest Editors: Michele Ceriotti, Cecilia Clementi, and Anatole von Lilienfeld with JCP Editors David Manolopoulos, David Sherrill, and Angelos Michaelides