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High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
Open accessAbstract
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 AB 2 C and predict the existence of 12 novel gallides M Ru 2 Ga and Ru M 2 Ga (M = Ti–Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu 2 Ga, 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 high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Tunable multifunctional topological insulators in ternary Heusler compounds
Chadov, S., Qi, X., Kübler, J., Fecher, G. H., Felser, C., & Zhang, S. C. (2010) · Nature Materials · doi:10.1038/nmat2770 · Cited by 927
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Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials · doi:10.1021/cm100795d · Cited by 580
Cited by
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
Metadata
- Authors
- Oliynyk, A. O., Antono, E., Sparks, T. D., Ghadbeigi, L., Gaultois, M. W., Meredig, B., & Mar, A.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-10-04
- Volume
- 28
- Issue
- 20
- Pages
- 7324-7331
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2520500207
- Cited by
- 387
- References
- 4
- FWCI
- 13.68
- Type
- journal article
- Language
- en
- Open access
- green
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- Oliynyk, A. O., Antono, E., Sparks, T. D., Ghadbeigi, L., Gaultois, M. W., Meredig, B., & Mar, A. (2016). High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds. Chemistry of Materials. https://doi.org/10.1021/acs.chemmater.6b02724
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