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
RANK_REASON The cluster contains a review paper on a research topic.
- Active Learning
- arXiv
- Bayesian Optimization
- Diffusion Models
- Generative Models
- Inverse Materials Design
- Multimodal Learning
- Normalizing Flows
- Reinforcement Learning
- Variational Autoencoders
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