Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
arXiv:2606.02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints. Here, we review recent advances in generative crystal structure modeling, multimodal learning, and closed-loop design pipelines for crystalline solids. We survey how modern generators learn chemical-structural priors from large databases to enable controllable sampling of periodic structures, and compare leading model classes including variational autoencoders, normalizing flows,
The accelerating pace of generative AI and multimodal learning in diverse fields has enabled its application to complex scientific domains like materials design.
Automated inverse materials design can dramatically accelerate the discovery of novel materials, impacting numerous industries from energy to defense.
Materials science moves from hypothesis-driven experimentation to AI-guided, targeted discovery, significantly reducing development timelines and costs.
- · Materials science
- · Deep Tech startups
- · AI/ML researchers
- · Manufacturing sector
- · Traditional materials R&D labs
AI-driven discovery leads to a surge in new material patents and applications.
Access to advanced materials becomes a new strategic advantage for nations and corporations.
The development cycle for new technologies (e.g., batteries, semiconductors) accelerates dramatically due to on-demand material innovation.
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Read at arXiv cs.LG