AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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CrysVCD is a modular framework that integrates chemical rules directly into the generative process, enabling orders-of-magnitude more efficient chemical valence checking compared with pure data-driven approaches with post-screening.
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet many existing approaches overlook essential chemical constraints, such as oxidation-state balance, which can lead to chemically invalid structures. Here we introduce 'crystal generator with valence-constrained design...
Self-supervised pretraining substantially improves TS prediction for previously unseen systems, lowering the median root-mean-square deviation of TS geometries on Transition1x-TMC reactions and reducing fine-tuning data requirements, enabling reliable performance even in low-data regimes.
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