Full
WU Kedeng (1999-), male, Master candidate. E-mail: wkd135790_29@163.com
Outline
The inverse design of crystalline materials aims to generate novel structures with targeted properties. However, achieving this objective remains a long-standing challenge in materials science. Unlike molecular systems that benefit from mature representations such as SMILES, crystalline materials impose stringent physical constraints, requiring representations that are simultaneously periodicity-aware, invertible, and invariant under symmetry operations. Emerging generative artificial intelligence (AI) technologies present transformative opportunities to address these complexities.
This mini-review systematically examines the latest advancements in this field, centering on two critical pillars: crystal representation methods and generative model architectures. In terms of crystal representations, we critically evaluate text-based symbolic encodings (e.g., SLICES), geometry-based periodicity-aware graphs, and pixel-based imaging approaches. These methods are analyzed based on their ability to balance invertibility with symmetry preservation. With respect to generative model architectures, we trace the technological evolution from latent space-based models, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), to state-of-the-art frameworks including diffusion models, flow-matching, and Transformer-based autoregressive models (e.g., MatterGen, MatterGPT). The review highlights how these architectures integrate physical constraints to navigate the vast chemical space of crystalline materials efficiently.
Despite significant progress, the field of AI-driven crystal design faces critical bottlenecks. A primary challenge lies in the absence of a universally accepted representation framework that simultaneously satisfies invertibility, invariance, and periodicity encoding capabilities. Furthermore, current generative models predominantly focus on ideal crystal structures, often neglecting real-world complexities such as defects, impurities, and service environments. To bridge this gap and facilitate the transition from theoretical exploration to practical application, future research should focus on three key directions: 1) Physics-informed representation design: Developing representations that intrinsically embed crystallographic constraints, such as Wyckoff positions and space groups, to ensure symmetry-aware and invertible encodings. 2) High-fidelity modeling of real-world materials: Extending beyond idealized lattice structures to model realistic defects and environmental responses (e.g., temperature, pressure) by integrating symmetry-constrained data augmentation with molecular dynamics simulations. 3) Standardization of benchmarks: Establishing community-wide benchmarks that rigorously evaluate generation stability, novelty, diversity, and computational cost, ensuring objective comparisons across different chemical systems. Addressing these challenges will be pivotal in realizing the automated, intelligent design of functional crystalline materials.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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