Researchers have developed a new, data-efficient method for crystal design that can satisfy multiple properties and physical constraints simultaneously. This approach utilizes predictor-guided gradient optimization, combining off-the-shelf property predictors with site-wise element masks and task-specific losses. The method demonstrated superior performance in perovskite design compared to generative and Bayesian baselines, achieving competitive band-gap targeting with significantly less training data. This adaptable framework also successfully supported half-metal design, offering a modular solution for optimizing candidate crystals with minimal computational cost. AI
RANK_REASON The cluster contains an academic paper detailing a new methodology for crystal design using AI. [lever_c_demoted from research: ic=1 ai=1.0]
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