Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning

• URL: http://arxiv.org/abs/2511.07158v1
• Date: Mon, 10 Nov 2025 14:48:49 GMT
• Title: Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning
• Authors: Hyunsoo Park, Aron Walsh,
• Abstract summary: We introduce a reinforcement learning framework that guides latent denoising diffusion models toward diverse, yet thermodynamically viable crystalline compounds.<n>Our approach integrates group relative policy optimisation with verifiable, multi-objective rewards that jointly balance creativity, stability, and diversity.<n>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.
• Score: 13.437119411600499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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