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Multimodal Learning

techniqueactiveprovisionalmultimodal-learning-c5abe0b5·1 events·first seen 15d ago

Aliases: Multimodal Learning

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5arXiv · cs.LG·15d ago·source ↗

Review: Generative Models, Multimodal Learning, and Closed-Loop Workflows in Inverse Materials Design

This arxiv review surveys recent advances in generative modeling for inverse materials design, covering variational autoencoders, normalizing flows, autoregressive models, and diffusion models applied to crystalline solid discovery. It examines how multimodal learning fuses crystal structures, thermodynamic data, spectroscopy, microscopy, and scientific text into transferable chemical-space representations. The paper also reviews closed-loop design pipelines integrating conditional generation with Bayesian optimization, reinforcement learning, and active learning, and identifies recurring failure modes including surrogate exploitation, diversity collapse, and the stability-synthesizability gap.