| Abstract Scope |
Understanding the phase stability of a chemical system constitutes the foundation of materials science. Knowledge of the equilibrium state of a system under arbitrary thermodynamic conditions provides valuable information about the types of phases that are likely to be synthesized and how to get there. While the materials science community has long been focused on exploiting this knowledge to navigate the materials space, recent advances in machine learning (ML) and artificial intelligence (AI) have provided the community with novel ways of interrogating the materials thermodynamics space. In this talk, I will present some of the most recent advances in ML/AI applied to phase stability and thermodynamics of materials, including some recent work by my group on accelerated search over large alloy spaces, ML-based accelerated prediction of thermodynamic properties, active learning approaches to phase diagram determination as well as application of UQ/UP to CALPHAD. |