用强化学习引导生成模型发现新且稳定的晶体材料。
Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning
- 结合强化学习与多目标奖励，引导扩散模型生成新晶体。
- 在保持化学合理性前提下，生成多样且热力学稳定的新化合物。
- 适合材料科学中的逆向设计，兼顾新颖性与可行性。
发现功能性晶态材料需在庞大的组合设计空间中探索。尽管生成式人工智能已能采样化学上合理的成分与结构，但生成模型基于似然的采样与聚焦于未探索区域（新化合物所在）的目标之间存在根本性错配。本文提出一种强化学习框架，引导潜在去噪扩散模型生成多样、新颖且热力学可行的晶态化合物。该方法融合群体相对策略优化与可验证的多目标奖励，协同平衡创造性、稳定性与多样性。除从头生成外，还展示了保留化学合理性的属性导向设计，能有效实现所需功能属性的靶向设计。该方法为可控的AI驱动逆向设计建立了模块化基础，解决了生成模型在科学发现中普遍存在的新颖性-有效性权衡问题。
原文摘要 · Abstract (English)
Discovering functional crystalline materials entails navigating an immense combinatorial design space. While recent advances in generative artificial intelligence have enabled the sampling of chemically plausible compositions and structures, a fundamental challenge remains: the objective misalignment between likelihood-based sampling in generative modelling and targeted focus on underexplored regions where novel compounds reside. Here, we introduce a reinforcement learning framework that guides latent denoising diffusion models toward diverse and novel, yet thermodynamically viable crystalline compounds. Our approach integrates group relative policy optimisation with verifiable, multi-objective rewards that jointly balance creativity, stability, and diversity. Beyond de novo generation, we demonstrate enhanced property-guided design that preserves chemical validity, while targeting desired functional properties. This approach establishes a modular foundation for controllable AI-driven inverse design that addresses the novelty-validity trade-off across scientific discovery applications of generative models.
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