Institute of Nanotechnology, Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学纳米技术研究所）；Institute of Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学人机学与机器人研究所）；Department of Materials Science and Engineering, University of California, Berkeley（加州大学伯克利分校材料科学与工程系）；Materials Sciences Division, Lawrence Berkeley National Laboratory（伯克利国家实验室材料科学部）
Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi, Jonas Teufel, Henrik Schopmans, Jona Östreicher, Yumeng Zhang, Marlen Neubert, Yuri Koide, Kevin St… 展开作者
Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi, Jonas Teufel, Henrik Schopmans, Jona Östreicher, Yumeng Zhang, Marlen Neubert, Yuri Koide, Kevin Steiner, Paul Link, Lukas Bär, Mariana Petrova, Gerbrand Ceder, Pascal Friederich
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.
Space Group Informed Transformer for Crystalline Materials Generation
Beijing National Laboratory for Condensed Matter Physics（北京凝聚态物理国家实验室）；School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China（中国科学院大学物理科学学院，北京100190，中国）；Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, China（教育部材料模拟方法与软件重点实验室，吉林大学物理学院，长春130012，中国）；State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, P. R. China（超硬材料国家重点实验室，吉林大学物理学院，长春130012，中华人民共和国）；Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China（松山湖材料实验室，东莞，广东523808，中国）
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Harvey Mudd College, USA（哈维·穆德学院，美国）；National University of Singapore, Singapore（新加坡国立大学）；University of Cambridge, UK（剑桥大学）；Chalmers University of Technology, Sweden（瑞典查尔姆斯理工大学）；Indian Institute of Technology Delhi, India（印度德里理工学院）
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）
DiffCrysGen: A Generative Diffusion Model for Accelerated Design of Inorganic Crystalline Materials
Harish-Chandra Research Institute（哈里什-昌德拉研究所）；Homi Bhabha National Institute（霍米·巴巴国立研究所）；Indian Institute of Science Education and Research (IISER) Tirupati（印度科学教育研究 institute 提鲁帕蒂分校）；National Institute of Science Education and Research (NISER)（国立科学教育研究所）
Compositional Representation of Polymorphic Crystalline Materials
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Korea Research Institute of Chemical Technology (KRICT)（韩国化学技术研究院（KRICT））；University of Illinois Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校（UIUC））；Rensselaer Polytechnic Institute (RPI)（伦斯勒理工学院（RPI））；Harvard University（哈佛大学）
Local thickness and composition measurements from scanning convergent-beam electron diffraction of a binary non-crystalline material obtained by a pixelated detector
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials
SAKURA internet Inc.（樱花互联网公司）；National Institute for Materials Science (NIMS)（国立材料科学研究所）；The University of Tokyo（东京大学）；RIKEN（理化学研究所）；Kyoto University（京都大学）
Quantized Failure Criteria and Indirect Observation for Predicting the Nanoscale Strength of Materials: The Example of the Ultra Nano Crystalline Diamond
Study of the terahertz spectra of crystalline materials using NDDO semi-empirical methods: polyethylene, poly(vinylidene fluoride) form II and $α$-D-glucose
Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor
Northwestern Polytechnical University（西北工业大学）；CRISMAT, CNRS, Normandie Univ, ENSICAEN, UNICAEN（诺曼底大学，卡昂国立高等工程学院，法国国家科学研究中心材料研究化学实验室）；Thayer School of Engineering, Dartmouth College（达特茅斯学院塔克工程学院）；Institute Laue Langevin（朗之万研究所）；FZU - Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）；ShanghaiTech Laboratory for Topological Physics & School of Physical Science and Technology, ShanghaiTech University（上海科技大学物理科学与技术学院拓扑物理实验室）；Université de Lorraine, CNRS, IJL（洛林大学，法国国家科学研究中心，界面与层析实验室）
A Generalized Frank-Bilby Equation for Interfaces in Crystalline Materials
Department of Materials Science and Engineering, City University of Hong Kong（香港城市大学材料科学与工程系）；College of Mathematics and Statistics, Shenzhen University（深圳大学数学与统计学院）；Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；Department of Mathematics, The Hong Kong University of Science and Technology（香港科技大学数学系）
