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晶体材料的物理化学性质根本上取决于其三维空间中原子周期性排列所形成的晶体结构。晶体结构的高效预测与合理设计是新材料研发的关键环节，传统基于密度泛函理论(DFT)的高通量筛选和全局优化方法虽在简单体系中表现突出，但计算开销随原子数增加呈指数增长、对复杂多组分体系适应性差。近年来，人工智能的发展为晶体结构研究提供了新思路，其中，生成式模型(generative models)通过学习晶体数据库中结构与成分的分布规律，可在条件约束下高效探索庞大的化学空间，生成具备目标性能的候选结构。该类模型兼具低推理成本与强泛化能力，为突破传统方法瓶颈提供了关键路径。本文综述生成式模型在晶体结构研究中的最新进展，涵盖生成任务定义、结构表示方法、主要数据集、模型演进及典型应用，并展望其在材料智能设计和理论计算–实验验证闭环系统方面的未来发展方向。
Abstract：Generative models for crystalline materials have emerged as a transformative paradigm in computational materials science, offering new possibilities for crystal structure prediction(CSP), inverse structural design, and functional materials discovery. Compared with traditional search-based CSP frameworks that depend on iterative global optimization and handcrafted physical constraints, generative approaches learn the statistical distribution of atomic arrangements directly from data, thereby enabling efficient sampling of chemically valid and symmetry-consistent structures. Recent advances in geometric deep learning, equivariant neural networks, variational autoencoders, and diffusion-based modeling have substantially enhanced both the fidelity and scalability of generative crystal models. However, despite the rapid development, a comprehensive and systematic review that integrates representations, generative mechanisms, and practical applications of these models is still sparse. This review first analyzes the major classes of crystal representations. According to the level of geometric completeness and symmetry preservation, representations can be categorized into reciprocal-space encodings(e.g., diffraction-based descriptors), symmetry-aware Wyckoff-position formalisms, voxelized three-dimensional grids, point-cloud coordinate systems, and graph-based multirelational structures. The specific characteristics of each representation are summarized, including their capabilities for handling periodicity, long-range interactions, spatial boundary conditions, and compatibility with equivariant neural networks. Hybrid schemes—such as the point-cloud/graph combined formulations used in DiffCSP, MatterGen, and GNOME—provide balanced expressiveness and geometric accuracy, enabling efficient modeling of complex crystalline systems. The generative mechanisms of crystal models are more intricate than those of molecular generative models. According to their learning paradigm, recent models may be divided into four categories. Multimodal-aligned models(e.g., Chemeleon) employ contrastive learning to align textual descriptions with crystal structures, enabling text-conditioned generation across complex chemical spaces. Experiment-driven models(e.g., XtalNet) directly map PXRD spectra to crystal structures via integrated pretraining and conditional generation, circumventing traditional database matching. Twin-model collaborative frameworks(e.g., DAO) combine generative modules with high-precision property predictors, achieving concurrent optimization of structural stability and targeted properties. Dynamics-informed models incorporate nonlinear kinetic effects such as impurity-induced frustration and non-equilibrium crystallization into sampling strategies. These mechanisms collectively provide diverse pathways for addressing the unique challenges posed by crystalline systems. The applications of generative crystal models are further examined across pharmaceuticals, quantum materials, and energy-related functional materials. In pharmaceutical solid-state chemistry, pocket-conditioned molecular generative models provide insights into integrating solid-form considerations with molecular design. In quantum materials research, constraint-embedded generators such as SCIGEN enable large-scale exploration of lattice-specific structures—including Archimedean and Lieb lattices—yielding candidates for quantum spin liquids, flat-band materials, and exotic magnetic phases. In energy materials, the DAO framework demonstrates the potential of “generation–prediction” coupling by successfully designing superconductors and high-capacity layered cathodes, showing strong coherence between computational predictions and experimental verification. Summary and prospects Although notable progress has been made in generative crystal models, significant challenges remain in both scientific understanding and practical deployment. Existing datasets are limited in structural diversity, physical fidelity, and multimodal coverage, making it necessary to construct large-scale, high-quality datasets integrating experimental observations, first-principles calculations, and model-generated structures. The physical interpretability and reliability of generated structures, particularly in multicomponent and nonequilibrium systems, require systematic investigation. Establishing closed-loop platforms that integrate model generation, autonomous synthesis, high-throughput characterization, and iterative retraining remains a key step toward practical materials discovery. Physics-aware constraints, uncertainty quantification, and robust generation under sparse data conditions are essential for improving model reliability. Furthermore, industrial applications demand models capable of handling large unit cells, defect chemistry, compositional tunability, and synthesis feasibility. With the development of large materials models, automated laboratories, and data-centric infrastructures, generative crystalline modeling is expected to become a core driver in building an intelligent, automated, and industrialized materials innovation ecosystem.
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基本信息:
DOI：10.14062/j.issn.0454-5648.20250875
中图分类号:O76;TP18
引用信息:
[1]魏巍,严壮,唐智勇.晶体结构生成式模型研究进展[J].硅酸盐学报,2026,54(01):106-121.DOI:10.14062/j.issn.0454-5648.20250875.
基金信息:
北京市科学技术委员会中关村科技园区管理委员会资助（Z251100007525007）
2025-11-30
2025
2026-01-04
2026
2
2026-01-08
2026-01-08
2026-01-08