Interfacial informatics

Abstract

Abstract Using machine learning methods to analyse and predict events occurring at interfaces is more complicated than applying machine learning to participating entities, such as adsorbates and adsorbents separately. Whether combining molecular or materials descriptors, or explicitly defining the topology of the space in between, the choice of features is critical to successfully understanding the potential energy surface that determines the probability of events, or the importance of unique characteristics that can guide decision making. If reliably defined these descriptors can be used in advanced machine learning methods to model dynamics, co-adsorption and interfacial evolution based on complex data; an area traditionally reserved for molecular dynamics or kinetic Monte Carlo. In this perspective, we provide some insights into how interactions and interfaces can be more effectively described and introduce some relevant machine learning methods that go beyond the unsupervised pattern recognition or supervised classification and regression currently preferred by the community.