High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org.
A comment on "The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals"
最近，Sten Haastrup，Mikkel Strange，Mohnish Pandey，Thorsten Deilmann，Per S Schmidt，Nicki F Hinsche，Morten N Gjerding，Daniele Torelli，Peter M Larsen，Anders C Riis-Jensen，Jakob Gath，Karsten W Jacobsen，Jens Jrgen Mortensen，Thomas Olsen和Kristian S Thygesen [2D Mater. 5（2018）042002]介绍了计算二维材料数据库（C2 DB），该数据库组织了分布在30多种不同晶体结构中的约1500种二维材料的各种结构，热力学，弹性，电子，磁性和光学性质。不幸的是，这项工作在弹性和机械稳定性测试领域存在严重和根本性的缺陷，这使得它不可靠。
The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns
State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院金属基复合材料国家重点实验室）
High-throughput computational framework for lattice dynamics and thermal transport including high-order anharmonicity: an application to cubic and tetragonal inorganic compounds
Northwestern University（西北大学）；Portland State University（波特兰州立大学）
High-Throughput Discovery of Semimetallic Borophenes with Diverse Dirac States Via Transferable Tight-Binding Approach
School of Physics and Optoelectronics, Xiangtan University（湘潭大学物理与光电工程学院）；Hunan Provincial Key Laboratory of Computational Condensed Matter Physics and Quantum Materials Engineering（湖南省计算凝聚态物理与量子材料工程重点实验室）
Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening
Soochow University（苏州大学）；AI for Science Institute（人工智能科学研究院）；Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University（香港理工大学智能穿戴系统研究所）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院生物医学工程系）
High-Throughput Discovery of Chiral Carbon-Boron Semimetals with Weyl Topology and Phonon-Mediated Superconductivity
University of Science and Technology of China（中国科学技术大学）；Wuhan Institute of Technology（武汉工程大学）；Hefei University of Technology（合肥工业大学）；Hanhai Lingchao Intelligent Computing (Shanghai) Technology Services Co., Ltd.（瀚海凌潮智能计算（上海）科技有限公司）
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System
快速构建相图是组合材料科学的核心原则，加速材料发现的努力往往受到解释组合X射线衍射数据集的挑战的阻碍，我们通过开发AgileFD来解决这个问题，AgileFD是一种人工智能算法，可以从X射线衍射图案的组合库中快速绘制相位图。AgileFD通过卷积非负矩阵分解的新扩展对基于合金的峰位移进行建模，这不仅改善了组成相的识别，而且还将其浓度和晶格参数映射为组成的函数。通过将Gibbs相位规则引入算法，在无监督操作的情况下得到有物理意义的相位图，并通过注入系统的专家知识得到更精细的解。该算法是证明通过调查的V-Mn-Nb氧化物系统，其中分解的8个氧化物相，包括两个大量的合金化，提供了第一个相图为这个伪三元系统。该相图能够解释高通量带隙数据，从而发现新的太阳光吸收剂和基于合金的MnV 2 O 6直接允许带隙能量的调谐。AgileFD算法的开源系列可以在广泛的高通量工作流程中实施，以加速材料发现。
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）
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）
High-Throughput Computational Discovery of Inverted Resistive Switching in Two-Dimensional Materials
Science, Mathematics and Technology (SMT) Cluster, Singapore University of Technology and Design（新加坡科技设计大学）；Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（科学技术研究局）；School of Information Science and Technology, Northwest University（西北大学）；School of Physics and Optoelectronics, Xiangtan University（湘潭大学）；Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Physical Science Research Center, Nanjing University（南京大学）；Quantum Innovation Centre (Q. InC), Agency for Science Technology and Research (A*STAR)（科学技术研究局）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学）；Department of Materials Science and Engineering, National University of Singapore（新加坡国立大学）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学）
