主动学习已经越来越多地应用于从现有材料数据库中筛选具有所需性质的功能材料。然而，存放在流行的材料数据库（如ICSD和Materials Project）中的已知材料数量非常有限，仅占巨大化学设计空间的一小部分。本文提出了一种主动生成式逆设计方法，该方法将主动学习与深度变分自编码器神经网络和生成对抗深度神经网络模型相结合，以在整个化学设计空间中发现具有目标性质的新材料。该方法的应用使我们能够发现具有高带隙的新的化学稳定材料（SrYF $_5$）和具有特定带隙范围的半导体（SrClF $_3$，CaClF $_5$，YCl $_3$，SrC $_2$ F $_3$，AlSCl，As $_2$ O $_3$），所有这些都通过第一原理DFT计算验证。我们的实验表明，虽然主动学习本身可能会采样化学上不可行的候选人，这些样本有助于训练有效的筛选模型，用于从生成模型创建的假设材料中筛选出具有所需特性的材料。实验结果表明了该方法的有效性。
英文摘要：
Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein we present an active generative inverse design method that combines active learning with a deep variational autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF$_5$) and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$, YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the first principle DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach.
Inverse Materials Design by Large Language Model-Assisted Generative Framework
School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；The University of Hong Kong（香港大学）；City University of Hong Kong（香港城市大学）；DP Technology（DP科技）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；AI for Science Institute, Beijing（北京科学智能研究院）
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab（多媒体信息处理国家重点实验室，计算机科学学院，PKU-Anker LLM实验室）；Peking University（北京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Paul G. Allen School of Computer Science（保罗·G·艾伦计算机科学学院）；University of Washington（华盛顿大学）；Hong Kong University of Science and Technology（香港科技大学）
Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
School of Physics and Electronic Engineering, Jiangsu University（江苏大学物理与电子工程学院）；Jiangsu Engineering Research Center on Quantum Perception and Intelligent Detection of Agricultural Information（江苏省量子感知与农业信息智能检测工程研究中心）
A generative machine learning model for designing metal hydrides applied to hydrogen storage
Mathematics and Statistics, Louisiana Tech University（数学与统计学系，拉法叶技术大学）；Louisiana Tech University（拉法叶技术大学）；Institute for Micromanufacturing, Louisiana Tech University（微制造研究所，拉法叶技术大学）；National Energy Technology Laboratory, United States Department of Energy, Pittsburgh, PA 15236（美国能源部国家能源技术实验室，匹兹堡，PA 15236）
Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Technical University of Munich（慕尼黑技术大学）；TUM School of Engineering and Design（慕尼黑技术大学工程与设计学院）；Department of Engineering Physics and Computation（工程物理与计算系）；Munich Data Science Institute（慕尼黑数据科学研究所）；AMC（原子模型中心）
Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
University of Vermont（弗吉尼亚大学）；Larner College of Medicine, University of Vermont（弗吉尼亚大学医学学院）；Purdue University（普渡大学）；Department of Comparative Pathobiology（比较病理科部门）；Department of Horticulture and Landscape Architecture（园艺与景观建筑部门）；Department of Industrial and Molecular Pharmaceutics（工业与分子药学部门）
Tuning the optoelectronic properties of wide bandgap perovskites: Data-driven insights from combinatorial synthesis and high-throughput experimentation
Empa–Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术研究院）
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Quantum Measurement Group, MIT（麻省理工学院量子测量组）；Department of Materials Science and Engineering, MIT（麻省理工学院材料科学与工程系）；Department of Chemistry, MIT（麻省理工学院化学系）；Neutron Scattering Division, Oak Ridge National Laboratory（橡树岭国家实验室中子散射部）；Department of Chemistry, Michigan State University（密歇根州立大学化学系）；Department of Nuclear Science and Engineering, MIT（麻省理工学院核科学与工程系）
Discovery of Stable Hybrid Organic-inorganic Double Perovskites for High-performance Solar Cells via Machine-learning Algorithms and Crystal Graph Convolution Neural Network Method
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
1 Department of Computer Science, University of Chicago；2 Data Science \& Learning Division, Argonne National Laboratory；3 Department of Civil, Materials；Environmental Engineering, University of Illinois at Chicago
Generative Chemical Language Models for Energetic Materials Discovery
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）；Weapon Stockpile Modernization Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室武器库存现代化部）；Computing and Artificial Intelligence Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室计算与人工智能部）
Materials discovery acceleration by using condition generative methodology
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Department of Chemistry, Key Laboratory of Computational Physical Science (Ministry of Education), Fudan University（复旦大学化学系）；Chongqing Bitmap Information Technology Co. Ltd（重庆比特玛信息有限公司）
Artificial Intelligence and Generative Models for Materials Discovery -- A Review
Institute of Sustainability for Chemicals, Energy and Environment (ISCE 2 )（化学、能源与环境可持续性研究所）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Institute of Materials Research and Engineering (IMRE)（材料研究与工程研究所）
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration
Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；Titan Holdings（泰坦控股）；University of Chinese Academy of Sciences（中国科学院大学）
Physics adapted generative AI for metal insulator transition materials under label scarcity
Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Lab（创新研究工程智能神经形态与量子理解实验室）；School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）
Generative Discovery of Magnetic Insulators under Competing Physical Constraints
Department of Math, University of Southern California（数学系，南加州大学）；Department of Computer Science, Colby College（计算机科学系，科尔比学院）；California NanoSystems Institute, University of California, Los Angeles（加州大学洛杉矶分校加州纳米系统研究所）
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）
