https://materialsproject.org/ ta-Driven Discovery of Photoactive Quaternary Oxides Using st-Principles Machine Learning iel W. Davies,† Keith T. Butler,‡ and Aron Walsh*,†,§ artment of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, U.K. ML, Scientific Computing Division, Rutherford Appleton Laboratory, Harwell Oxford, Didcot, Oxfordshire OX11 0QX, U.K. bal E3 Institute and Department of Materials Science and Engineering, Yonsei University, Seoul 120-749, Korea Supporting Information BSTRACT: We present a low-cost, virtual high-throughput terials design workflow and use it to identify earth- undant materials for solar energy applications from the aternary oxide chemical space. A statistical model that edicts bandgap from chemical composition is built using pervised machine learning. The trained model forms the t in a hierarchy of screening steps. An ionic substitution orithm is used to assign crystal structures, and an oxidation te probability model is used to discard unlikely chemistries. e demonstrate the utility of this process for screening over 1 llion oxide compositions. We find that, despite the fficulties inherent to identifying stable multicomponent organic materials, several compounds produced by our rkflow are calculated to be thermodynamically stable or metastable and have desirable optoelectronic properties according to t-principles calculations. The predicted oxides are Li2 MnSiO5 , MnAg(SeO3 )2 , and two polymorphs of MnCdGe2 O6 , all four Article pubs.acs.org/cm Cite This: Chem. Mater. 2019, 31, 7221−7230