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）
Ze-Feng Gao, Shuai Qu, Bocheng Zeng, Yang Liu, Ji-Rong Wen, Hao Sun, Peng-Jie Guo, Zhong-Yi Lu
备注：
46 pages; 23 figures; 4 tables
发表信息：
National Science Review, Volume 12, Issue 4, April 2025
Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties, the limited availability of known altermagnetic materials hinders the study of such properties. Hence, discovering more types of altermagnetic materials with different properties is crucial for a comprehensive understanding of altermagnetism and thus facilitating new applications in the next generation information technologies, e.g., storage devices and high-sensitivity sensors. Since each altermagnetic material has a unique crystal structure, we propose an automated discovery approach empowered by an AI search engine that employs a pre-trained graph neural network to learn the intrinsic features of the material crystal structure, followed by fine-tuning a classifier with limited positive samples to predict the altermagnetism probability of a given material candidate. Finally, we successfully discovered 50 new altermagnetic materials that cover metals, semiconductors, and insulators confirmed by the first-principles electronic structure calculations. The wide range of electronic structural characteristics reveals that various novel physical properties manifest in these newly discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr effect, and topological property. Noteworthy, we discovered 4 $i$-wave altermagnetic materials for the first time. Overall, the AI search engine performs much better than human experts and suggests a set of new altermagnetic materials with unique properties, outlining its potential for accelerated discovery of the materials with targeted properties.
Atomic-Scale Observation of Symmetry Breaking in Altermagnetic MnTe
University of Washington（华盛顿大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Uppsala University（乌普萨拉大学）；Rice University（莱斯大学）；Czech Academy of Sciences（捷克科学院）；Brno University of Technology（布尔诺理工大学）；University of Nottingham（诺丁汉大学）；Tohoku University（东北大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Machine-learning-accelerated discovery of synthesizable high-temperature altermagnets with giant spin splitting
Beihang University（北京航空航天大学）；Peng Huanwu Collaborative Center for Research and Education, Beihang University（北京航空航天大学彭桓武研究中心）；Institute of Theoretical Physics, Shanxi University（山西大学理论物理研究所）；Institute of Theoretical Physics, Chinese Academy of Sciences（中国科学院理论物理研究所）
Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics
Shenzhen Geim Graphene Center & Shenzhen Key Laboratory of Advanced Layered Materials for Value-added Applications, Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院材料研究所）；School of Science, State Key Laboratory on Tunable Laser Technology and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）理学院）；State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University（清华大学物理系）；Institute for Advanced Study, Tsinghua University（清华大学高等研究院）；Frontier Science Center for Quantum Information（量子信息前沿科学中心）
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-enhanced discovery and accelerated synthesis of metal phosphosulfides
Technical University of Denmark（丹麦技术大学）；National Centre for Nano Fabrication and Characterization (DTU Nanolab)（国家纳米制造与表征中心（DTU纳米实验室））；Department of Energy Conversion and Storage（能源转换与存储系）
Emergent superconductivity at 16.3 K in an altermagnetic candidate Na$_{2-x}$V$_2$Se$_2$O with broken inversion symmetry
Institute of Physics and Beijing National Laboratory for Condensed Matter Physics, Chinese Academy of Sciences（中国科学院物理研究所及凝聚态物理北京国家实验室）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；College of Physics and Center of Quantum Materials and Devices, Chongqing University（重庆大学物理学院及量子材料与器件中心）；College of Physics and Electronic Engineering, Nanyang Normal University（南阳师范学院物理与电子工程学院）；School of Physics and Information Technology, Shaanxi Normal University（陕西师范大学物理学与信息技术学院）
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）
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）
Accelerated Discovery of Topological Conductors for Nanoscale Interconnects
NORDITA, KTH Royal Institute of Technology and Stockholm University（北欧理论粒子物理研究所，皇家理工学院和斯德哥尔摩大学）；University of Connecticut（康涅狄格大学）；Northwestern University（西北大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Academia Sinica（中央研究院）
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
太阳能在解决严重的环境问题和满足高能源需求方面发挥着重要作用。然而，缺乏合适的材料阻碍了这项技术的进一步发展。在这里，我们使用密度泛函理论（DFT）和机器学习方法，提出了迄今为止最大的无机太阳能电池材料搜索。我们计算了光谱有限的最大效率（SLME）使用Tran-Blaha修改的Becke-Johnson势为5097非金属材料，并确定了1997候选人的SLME高于10%，包括934候选人具有合适的凸壳稳定性和有效载流子质量。筛选二维分层的情况下，我们发现了58个潜在的材料，并进行G 0 W 0计算的子集，以估计预测的不确定性。由于上述DFT方法的计算成本仍然很高，我们开发了一种高精度的机器学习模型来预筛选有效的材料，并将其应用于超过一百万种材料。我们的研究结果为高效太阳能电池材料的设计提供了一个总体框架和通用策略。这些数据和工具在以下网址公开发布：https：//www.ctcms.nist.gov/jarvisml/、https://jarvis.nist.gov/和https://github.com/usnistgov/jarvis。
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）
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）
SCALE: Simulation-Calibrated Amortized Learning for Energy Materials (A hybrid architecture connecting deterministic modeling, real-world data, and transformer-scale inference for accelerated energy-materials discovery)
