Systematic Modification of Functionality Through Free Energy Surface Tailoring

Advances in manufacturing and characterization of complex molecular systems have created a need for new methods for design at molecular length scales. Emerging approaches are increasingly relying on the use of Artificial Intelligence (AI), and the training of AI models on large data libraries. This paradigm shift has led to successful applications, but shortcomings related to interpretability and generalizability continue to pose challenges. Here, we explore an alternative paradigm in which Machine Learning (ML) is combined with physics-based considerations for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using an ML model trained on data gathered from a single system. Through the ML-constructed collective variables, it becomes possible to identify critical interactions in the system of interest, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach we have applied it to numerous case studies, a few of which will be discussed and illustrated during this talk.