Topology-Stratified Materials Discovery with A Flow-Based Generative Model

arXiv:2609.26547 · cond-mat.mtrl-sci, cs.AI · Submitted 2026-09-22 · Read on arXiv

cond-mat.mtrl-sci, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/huhuhhhh/UFO-MGen

License: http://creativecommons.org/licenses/by/4.0/

The gist: Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion

Terminology

Abstract

Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, its performance remains limited by the complex crystal structures and diverse chemical compositions. In this work, we develop UFO-MGen, a universal flow-based generative model that learns topological features of Wyckoff representations and leverages this information to accurately generate crystals across vast structural and chemical spaces. Compared with state-of-the-art generative models, UFO-MGen achieves the highest crystal generation success rate under a rigorous multi-stability evaluation framework, the highest SUN (stable, unique, novel) rate, and a remarkable extrapolation capability that has not been reported by previous models. Furthermore, a fine-tuning module is implemented to UFO-MGen for property-constrained crystal generation, enabling the inverse materials design toward target properties. The UFO-MGen opens a new avenue for accelerated materials discovery and providing a foundation for universal materials intelligence.

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