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High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
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    High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
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    Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2 Canada
    Citrine Informatics, Redwood City, California 94063 United States
    § Department of Materials Science and Engineering, University of Utah, Salt Lake City, Utah 84112 United States
    Department of Chemistry, University of Cambridge, Cambridge CB2 1EW United Kingdom
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    Chemistry of Materials

    Cite this: Chem. Mater. 2016, 28, 20, 7324–7331
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    https://doi.org/10.1021/acs.chemmater.6b02724
    Published September 18, 2016
    Copyright © 2016 American Chemical Society

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    A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400 000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB2C and predict the existence of 12 novel gallides MRu2Ga and RuM2Ga (M = Ti–Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu2Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemmater.6b02724.

    • Machine-learning-predicted probabilities for the most likely Heusler and non-Heusler compounds; predicted probabilities for MRu2Ga, RuM2Ga, and LaM2Ga; probability distribution plots using 10-fold cross-validation and receiver operating characteristic curves; powder XRD analyses for ternary gallides; single-crystal XRD analysis for TiRu2Ga; and SEM/EDX analysis for TiRu2Ga (PDF)

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    80. Zhiyang Qin, Hongliang Zhao, Shuya Zhang, Yuheng Fan, Xianglei Dong, Zishuo Lan, Xiaobing Hu, Yang Song, Chunwen Guo. Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning. Materials Today Communications 2024, 39 , 108833. https://doi.org/10.1016/j.mtcomm.2024.108833
    81. Ayman Maqsood, Chen Chen, T. Jesper Jacobsson. The Future of Material Scientists in an Age of Artificial Intelligence. Advanced Science 2024, 11 (19) https://doi.org/10.1002/advs.202401401
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    83. Fan Yang, Guanjian Cheng, Wan-Jian Yin. Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm. Science China Materials 2024, 67 (4) , 1273-1281. https://doi.org/10.1007/s40843-024-2868-x
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    89. Ahsan Ali, Muhammad Adnan Khan, Hoimyung Choi. Prediction of hydrogen generation from perhydro-dibenzyltoluene empowered with machine learning. International Journal of Hydrogen Energy 2024, 51 , 171-178. https://doi.org/10.1016/j.ijhydene.2023.10.250
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    91. D.G. Gulevich, I.R. Nabiev, P.S. Samokhvalov. Machine learning–assisted colloidal synthesis: A review. Materials Today Chemistry 2024, 35 , 101837. https://doi.org/10.1016/j.mtchem.2023.101837
    92. R. M. Rowan-Robinson, Z. Leong, S. Carpio, C. Oh, N. A. Morley. Material informatics for functional magnetic material discovery. AIP Advances 2024, 14 (1) https://doi.org/10.1063/9.0000657
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    95. George Stephen Thoppil, Jian–Feng Nie, Alankar Alankar. Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys. Journal of Alloys and Compounds 2023, 967 , 171595. https://doi.org/10.1016/j.jallcom.2023.171595
    96. Jonathan Schmidt, Hai-Chen Wang, Georg Schmidt, Miguel A. L. Marques. Machine learning guided high-throughput search of non-oxide garnets. npj Computational Materials 2023, 9 (1) https://doi.org/10.1038/s41524-023-01009-4
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    99. Sean D. Griesemer, Yi Xia, Chris Wolverton. Accelerating the prediction of stable materials with machine learning. Nature Computational Science 2023, 3 (11) , 934-945. https://doi.org/10.1038/s43588-023-00536-w
    100. Ebrar Yildirim, Övgü Ceyda Yelgel. Using Machine Learning Techniques to Discover Novel Thermoelectric Materials. 2023https://doi.org/10.5772/intechopen.1003210
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    Cite this: Chem. Mater. 2016, 28, 20, 7324–7331
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    https://doi.org/10.1021/acs.chemmater.6b02724
    Published September 18, 2016
    Copyright © 2016 American Chemical Society

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