Inverse Defect Design Using AI and Machine Learning

Defect dynamics and design in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects—their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Using representative nanoscale materials, we will discuss our work on addressing this challenge – this involves utilizing AI/ML algorithms, including symbolic regression, active and transfer learning, multi-objective evolutionary optimization, and reinforcement learning (RL). Mechanistic understanding of the dynamics and design of defective materials can leverage such multi-fidelity models but often involve multi-objective, multi-dimensional search problems and learning domains that often have continuous search spaces. Conventional approaches rely on human intuition and metaheuristic searches, exhibiting issues like sluggish convergence and scalability concerns. Departing from traditional evolutionary, swarm, random sampling, and gradient-based methods, we will present our work on developing machine learning approaches to efficiently navigate high-dimensional search landscapes, significantly improving search quality, convergence speed, and scalability in material discovery and defect design.