Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 10 of its 47 references that are in the map. First posted on arXiv 27 Oct 2025.
Builds on
10 of 47 references.
Closest ancestors · 2
Same direction, newest first
- 2025Open Materials Generation with Stochastic Interpolants4 Feb
- 2023Crystal Structure Prediction by Joint Equivariant Diffusion30 Jul
Other references · 8
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
- 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
- 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 109
- 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
- 2023Scalable Diffusion for Materials Generationcited by 54
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
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- 2026Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
- 2025Establishing baselines for generative discovery of inorganic crystals
- 2026Generative structure search for efficient and diverse discovery of molecular and crystal structures
- 2024Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
- 2026Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation