Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.
Achieving Different Stoichiometries and Morphologies in Vapor Phase Deposition of Inorganic Halide Perovskites: Single or Dual Precursor Sources?
Brno University of Technology（布尔诺理工大学）；CEITEC BUT, Brno University of Technology（布尔诺理工大学CEITEC中心）；Forschungszentrum Jülich（于利希研究中心）；Faculty of Chemistry, Brno University of Technology（布尔诺理工大学化学学院）；NETZSCH-Gerätebau GmbH（耐驰仪器制造公司）
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Massachusetts Institute of Technology（麻省理工学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Dept. of Materials Science and Engineering, Massachusetts Institute of Technology（麻省理工学院材料科学与工程系）；Dept. of EECS and CSAIL, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系和计算机科学与人工智能实验室）；College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）
Long photoexcited carrier lifetime in a stable and earth-abundant zinc polyphosphide
Thayer School of Engineering, Dartmouth College（达特茅斯学院泰勒工程学院）；Department of Materials Science and NanoEngineering, Rice University（莱斯大学材料科学与纳米工程系）；Rice Advanced Materials Institute, Rice University（莱斯大学莱斯先进材料研究所）；Department of Chemistry, Iowa State University（爱荷华州立大学化学系）；National Laboratory of the Rockies, U.S. Department of Energy（美国能源部落基山国家实验室）；Department of Physics and Astronomy, Dartmouth College（达特茅斯学院物理与天文系）；Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego（加州大学圣地亚哥分校艾索余峰李家族化学与纳米工程系）；Ames National Laboratory, U.S. Department of Energy（美国能源部艾姆斯国家实验室）；Renewable and Sustainable Energy Institute, University of Colorado Boulder（科罗拉多大学博尔德分校可再生与可持续能源研究所）
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
University of Toronto（多伦多大学）；Acceleration Consortium（加速联盟）；National Research Council Canada（加拿大国家研究委员会）；Clean Energy Innovation Research Center（清洁能源创新研究中心）
