Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
A new review paper addresses the challenge of representing chemical disorder in materials for AI-driven discovery. It highlights the gap between experimental observations of disorder and the fully specified configurations typically required by simulations and AI models. The paper proposes a framework integrating classical and AI methods to bridge this gap, enabling AI to better handle disorder for more accurate materials discovery. AI
IMPACT Enables AI to better model and predict material properties by accounting for chemical disorder, potentially accelerating discovery.