Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to be challenging. Herein we propose a deep learning generative model for composition generation combined with random forest based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267,489 new potential 2D materials compositions and confirmed twelve 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.
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
How Evaluation Choices Distort the Outcome of Generative Drug Discovery
Institute for Complex Molecular Systems（复杂分子系统研究所）；Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands（生物医学工程系，埃因霍温理工大学，埃因霍温，荷兰）；Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, The Netherlands（生命技术中心，联盟TU/e，WUR，UU，乌得勒支大学医疗中心，荷兰）
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Edwardson School of Industrial Engineering and the Institute of Cancer Research, Purdue University（埃德沃兹工业工程学院和癌症研究所，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家庭电气与计算机工程学院，普渡大学）
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）
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）
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）
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）
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）
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）
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）
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）
