InvDesFlow-AL: active learning-based workflow for inverse design of functional materials

ArXi:2505.09203v2 Announce Type: replace-cross Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusion principles can directly produce new materials that meet performance constraints, thereby significantly accelerating the material design process. However, existing methods for generating and predicting crystal structures often remain limited by low success rates.