Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
A new review paper details advancements in using generative models and multimodal learning for inverse materials design. It covers various generative model classes like VAEs, normalizing flows, and diffusion models, emphasizing how physical constraints are integrated into the design workflow. The paper also explores how fusing diverse data modalities can create more universal representations of chemical space and discusses strategies for optimizing inverse design, alongside common failure modes and evaluation practices. AI
