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arXiv Machine Learning

Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors

The paper proposes ANCHOR, a composition policy that decouples crystal structure prediction (CSP) from de novo crystal generation (DNG) by using a frozen CSP model as a fixed evaluator and training the policy with multi‑objective rewards, including adaptive novelty. ANCHOR significantly improves metrics such as MSUN and SUN compared to traditional DNG approaches, and demonstrates transferability across different CSP backbones and distillation into Crystalite‑CSP. The study shows that fine‑tuning DNG models directly on these rewards yields limited gains, whereas the ANCHOR framework better leverages the CSP prior for more efficient crystal discovery.

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LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

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MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

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