CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
arXiv:2412.11693 · doi:10.1038/s41467-025-64364-4
Abstract
Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approach to efficiently explore the configuration space of crystalline materials. In this work, we present CrystalFlow, a flow-based generative model specifically developed for the generation of crystalline materials. CrystalFlow constructs Continuous Normalizing Flows to model lattice parameters, atomic coordinates, and/or atom types, which are trained using Conditional Flow Matching techniques. Through an appropriate choice of data representation and the integration of a graph-based equivariant neural network, the model effectively captures the fundamental symmetries of crystalline materials, which ensures data-efficient learning and enables high-quality sampling. Our experiments demonstrate that CrystalFlow achieves state-of-the-art performance across standard generation benchmarks, and exhibits versatile conditional generation capabilities including producing structures optimized for specific external pressures or desired material properties. These features highlight the model's potential to address realistic crystal structure prediction challenges, offering a robust and efficient framework for advancing data-driven research in condensed matter physics and material science.
Update references and acknowledgments
References in corpus (10)
- CALYPSO: a method for crystal structure prediction
- Crystal Structure Prediction via Particle Swarm Optimization
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
- Superconductivity at 250 K in lanthanum hydride under high pressures
- Ab initio Random Structure Searching
- Superconductive "sodalite"-like clathrate calcium hydride at high pressures
- An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
- Deep learning generative model for crystal structure prediction
- Con-CDVAE: A method for the conditional generation of crystal structures
- InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors