Crystal Structure Generation

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Crystal structure generation studies how to produce plausible periodic arrangements of atoms, supporting the search for materials with useful properties and the prediction of unknown structures. Common approaches include diffusion and flow-matching models, autoregressive generators, and models that encode crystallographic symmetry through space groups and Wyckoff positions. Researchers add chemical and physical constraints, property guidance, or energy-based screening, then evaluate candidates on benchmarks such as MP-20 and molecular-crystal datasets. A central challenge is balancing structural validity and stability with genuine novelty: models can reproduce known structural prototypes, while reliable validation often requires costly relaxation or first-principles calculations.

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NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction
Feb 19, 2026Jinming Mu, Lixin He, Xudong Zhu +1Crystal Structure Prediction Crystal Structure Generation
Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, Anhui, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, Anhui, China

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NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction

Jinming Mu, Lixin He, Xudong Zhu, Shi Yin

Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, Anhui, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, Anhui, China

Crystal Structure Prediction Crystal Structure Generation

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Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. Crystal symmetry plays a crucial role in CSP, but given the composition in a unit cell, existing methods either struggle with the NP-hard combinatorial challenge of enforcing symmetry rigorously or rely on retrieving known templates, inherently limiting both physical fidelity and the discovery of genuinely new materials. To address this challenge, we introduce NextCrystal, a symmetry-driven generative framework that employs large language models to encode chemical semantics and directly generate fine-grained Wyckoff site patterns from atomic stoichiometry, eliminating reliance on database lookups. To overcome the combinatorial complexity of site assignments, we incorporate domain knowledge via an efficient, linear-complexity heuristic beam search, rigorously enforcing algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, the framework constrains the stochastic generative trajectory to a physically plausible geometric manifold. NextCrystal achieves state-of-the-art performance on stability, uniqueness, and novelty (SUN) benchmarks, as well as superior structural matching, establishing a rigorous paradigm for exploring previously unexplored crystallographic space without relying on prior structural templates. As a representative application, first-principles screening of HfO2 candidates generated by NextCrystal identifies a previously unreported dynamically stable Pnma phase, 0.056~eV/atom lower in energy than the conventional high-pressure Pnma phase.