The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.
Molecular Crystal Structure Prediction from Conditional Flow on the Unit Cells
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；North Carolina Battery Complexity, Autonomous Vehicle and Electrification (BATT CAVE) Research Center（北卡罗来纳电池复杂性、自动驾驶与电气化（BATT CAVE）研究中心）
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
Institute of Theoretical Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院理论信息学研究所）；Institute of Nanotechnology, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院纳米技术研究所）
Department of Energy Conversion and Storage, Technical University of Denmark（能源转换与存储系，技术大学丹麦分校）；Department of Applied Mathematics and Computer Science, Technical University of Denmark（应用数学与计算机科学系，技术大学丹麦分校）
Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
Deep Principle, Inc.（Deep Principle 公司）；Frontiers Science Center for Transformative Molecules, School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院变革性分子前沿科学中心）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University（北京大学）；Cornell University（康奈尔大学）；Harvard Law School（哈佛法学院）
