Reinforcement learning steers generative crystal design

Researchers have developed a reinforcement learning framework that accelerates the discovery of stable crystal structures by guiding generative models toward materials with specific chemical properties. This approach addresses the computational bottleneck of evaluating vast atomic configurations, potentially streamlining the development of new materials for batteries and electronics.