Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space

TL;DR

This paper introduces a physics-informed generative AI model that efficiently proposes novel, physically plausible crystal structures by balancing structural novelty with stability, enhancing materials discovery.

Contribution

The work develops a diffusion model conditioned on structural novelty and stability metrics, integrating physics-based validation for targeted exploration of new chemical space.

Findings

Conditioning improves generative diversity and plausibility.

The model shifts generation away from dominant motifs.

Synergy with crystal structure prediction enhances discovery.

Abstract

Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidate crystal structures. We introduce a reliable low-cost proxy for structural novelty as a conditioning property to steer generation towards novel yet physically plausible structures. We then develop a physics-informed diffusion model that embeds this descriptor of local environment diversity together with compactness as a stability metric to balance physical plausibility with structural novelty. Conditioning on these metrics improves generative performance across diffusion models, shifting generation away from structural motifs that dominate the training data. A chemically grounded validation protocol isolates those candidates that combine plausibility with structural novelty for physics-based calculation of energetic stability. Both the stability and the novelty of candidates emerging from this workflow can however change when the full potential energy surface at a candidate composition is evaluated with crystal structure prediction (CSP). This suggests a practical generative-CSP synergy for discovery-oriented exploration, where AI targets physically viable yet structurally distinct regions of chemical space for detailed physics-based assessment of novelty and stability.