Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
Massachusetts Institute of Technology（麻省理工学院）；Tohoku University（东北大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Emory University（埃默里大学）
Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yon… 展开作者
Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, Mingda Li
备注：
512 pages total, 4 main figures + 218 supplementary figures
Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates.
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
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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）
Integration of Calcium Imaging Traces via Deep Generative Modeling
Dept. of Biomedical Sciences \& Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain Dept. of Condensed Matter Physics, Universitat de Barcelona, Barcelona, Spain
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）
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）
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）
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
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）
Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation
University of Tennessee（田纳西大学）；Oxford Instruments Asylum Research（牛津仪器庇护研究公司）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；National Cheng Kung University（国立成功大学）；the Pennsylvania State University（宾夕法尼亚州立大学）
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）
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）
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 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）
Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Deep Neural Networks with Application to Deep Eutectic Solvents
