Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. As a case study, we apply our new approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our new framework is generally applicable to all types of systems for precise and efficient structural search, providing new insights into the relationship between ML-extracted structural features and physical properties.
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）
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）
Deep Learning-Based Extraction of Promising Material Groups and Common Features from High-Dimensional Data: A Case of Optical Spectra of Inorganic Crystals
Institute of Science Tokyo（东京科学大学）；Tokyo Institute of Technology（东京工业大学）；Tohoku University（东北大学）；Kanagawa Institute of Industrial Science and Technology (KISTEC)（神奈川工业科学技术研究所）
Accelerated Inorganic Materials Design with Generative AI Agents
The University of Tokyo, Institute of Industrial Science（东京大学工业科学研究所）；University of Montreal, Department of Computer Science and Operations Research（蒙特利尔大学计算机科学与运筹学系）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
Faculty of Mechanical Engineering, Czech Technical University in Prague（布拉格捷克理工大学机械工程学院）；Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague（布拉格捷克理工大学核科学与物理工程学院）；FZU – Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）
Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)（柏林亥姆霍兹材料能源中心）；Faculty of Chemistry, University of Bielefeld（比勒费尔德大学化学系）；Center for Nano Science and Technology, Instituto Italiano di Tecnologia（意大利理工学院纳米科学与技术中心）；Department of Chemistry & IRIS Adlershof, Humboldt University（洪堡大学化学系与IRIS阿德勒霍夫）；Department of Chemical, Materials and Production Engineering, University of Naples Federico II（那不勒斯斐迪南二世大学化学、材料与生产工程系）；Institut Photovoltaïque d’Île-de-France (IPVF)（法兰西岛光伏研究所）；Université Paris-Saclay, CentraleSupélec, CNRS, Laboratoire de Génie Electrique et Electronique de Paris（巴黎萨克雷大学、中央理工-高等电力学院、法国国家科学研究中心巴黎电气工程实验室）；Sorbonne Université, CNRS, Laboratoire de Génie Electrique et Electronique de Paris（索邦大学、法国国家科学研究中心巴黎电气工程实验室）
Doubly Metastable Synthesis for Accelerated Quantum Materials Discovery
Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Chemical Physics of Solids（马克斯·普朗克固体化学物理研究所）；Cornell University（康奈尔大学）；Toho University（东海大学）
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA（材料科学与工程系，麻省理工学院）；Energy Storage Research Alliance, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, United States（能源存储研究联盟，阿贡国家实验室）；Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA（计算与系统生物学计划，麻省理工学院）
Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
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）
AI-accelerated Discovery of Altermagnetic Materials
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；School of Physics, Renmin University of China（中国人民大学物理学院）；School of Engineering Science, University of Chinese Academy of Sciences（中国科学院大学工程科学学院）
