Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\% compared to FTCP, one of the latest structure generators and by more than 45\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of which 39.6\% have negative formation energy and 5.3\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.
OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Drexel University, College of Computing and Informatics（德雷塞尔大学，计算机与信息学院）；Drexel University, Department of Chemical and Biological Engineering（德雷塞尔大学，化学与生物工程系）
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Department of Computer and Information Science (IDA), Linköping University, Sweden（计算机与信息科学系（IDA），_linköping大学，瑞典）；Department of Physics, Chemistry and Biology (IFM), Linköping University, Sweden（物理、化学与生物学系（IFM），_linköping大学，瑞典）
CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
AI for Science Institute, Beijing, China（人工智能科学研究院，北京，中国）；Center for Data Science, Peking University, Beijing, China（数据科学中心，北京大学，北京，中国）；LMAM and School of Mathematical Sciences, Peking University, Beijing, China（LMAM与数学科学学院，北京大学，北京，中国）；Center for Machine Learning Research, Peking University, Beijing, China（机器学习研究中心，北京大学，北京，中国）；National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, China（国家计算物理重点实验室，应用物理与计算数学研究所，北京，中国）；HEDPS, CAPT, College of Engineering, Peking University, Beijing, China（HEDPS、CAPT、工程学院，北京大学，北京，中国）
Accelerating single-crystal growth by stimulated and self-guided channeling
我们报告了一个自引导和“刺激”的单晶生长加速效应在静态过饱和水溶液中，生产无机（KH $_2$ PO $_4$）和有机（四苯基磷系列）非线性光学单晶与新的形态。在存在完全横向生长抑制的情况下的异常快速的单向生长违背了所有当前基于杂质、缺陷和位错的晶体生长抑制机制。我们提出了一个自沟道刺激的加速生长理论，可以令人满意地解释所有的实验结果。利用分子动力学分析和修正的双组分晶体生长模型，包括微观表面分子选择性，我们表明横向生长停滞是自沟道和自屏蔽效应的综合结果。这些单晶在缠绕和扭转方面表现出卓越的机械灵活性，展示了其在芯片尺寸量子和生物医学应用以及生产高产/高效药物材料方面的独特优势。
CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University（吉林大学物理学院教育部材料模拟方法与软件重点实验室）；State Key Laboratory of Superhard Materials, College of Physics, Jilin University（吉林大学物理学院超硬材料国家重点实验室）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
A Distributional Framework for Generative Modeling of Molecular Crystals
New York University（纽约大学）；Courant Institute of Mathematical Sciences, NYU（纽约大学库朗数学科学研究所）；NYU-ECNU Center for Computational Chemistry at NYU Shanghai（纽约大学上海纽约大学纽大-华东师范大学计算化学中心）；Simons Center for Computational Physical Chemistry at NYU（纽约大学西蒙斯计算物理化学中心）；Initiative for Computational Catalysis, Flatiron Institute（平顿研究所计算催化倡议）
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校材料科学与工程系）；Department of Computer Science, Princeton University（普林斯顿大学计算机科学系）；Materials Research Laboratory, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校材料研究实验室）
