DOI: 10.1021/acs.accounts.7b00490. Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

10.1021/acs.accounts.7b00490

Crossref journal-article

American Chemical Society (ACS)

Accounts of Chemical Research (316)

Bibliography

Oliynyk, A. O., & Mar, A. (2017). Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches. Accounts of Chemical Research, 51(1), 59â68.

Authors 2

Anton O. Oliynyk (first)
Arthur Mar (additional)

References 58 Referenced 118

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10.1021/cm802123a
10.1002/9783527615414 / Intermetallics by Sauthoff G. (1995)
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Mar, A.and co-workers. Unpublished work.
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10.1021/acs.chemmater.7b00156
10.1021/ci200628z
10.1002/minf.200900072
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{'volume-title': 'Pearson’s Crystal Data – Crystal Structure Database for Inorganic Compounds (on DVD)', 'year': '2016', 'author': 'Villars P.', 'key': 'ref37/cit37'} / Pearson’s Crystal Data – Crystal Structure Database for Inorganic Compounds (on DVD) by Villars P. (2016)
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10.1021/acs.chemmater.6b02724
10.1109/34.709601
10.1016/j.solidstatesciences.2004.07.011
Kiselyova, N. N.; Dudarev, V. A.; Korzhuev, M. A.Database on forbidden zones of solids, 2006–2017, (http://bg.imet-db.ru).
10.1063/1.4812323
Citrine Informatics,http://www.citrination.com.
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10.1038/nature17439

Dates

Type	When
Created	7 years, 8 months ago (Dec. 15, 2017, 2:04 p.m.)
Deposited	2 years, 4 months ago (April 17, 2023, 8:16 p.m.)
Indexed	3 days, 3 hours ago (Aug. 19, 2025, 6:03 a.m.)
Issued	7 years, 8 months ago (Dec. 15, 2017)
Published	7 years, 8 months ago (Dec. 15, 2017)
Published Online	7 years, 8 months ago (Dec. 15, 2017)
Published Print	7 years, 7 months ago (Jan. 16, 2018)

Funders 1

Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038
Region: Americas
gov (National government)
Labels3
1. Conseil de Recherches en Sciences Naturelles et en Génie du Canada
2. NSERC
3. CRSNG

BibTeX

@article{Oliynyk_2017, title={Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches}, volume={51}, ISSN={1520-4898}, url={http://dx.doi.org/10.1021/acs.accounts.7b00490}, DOI={10.1021/acs.accounts.7b00490}, number={1}, journal={Accounts of Chemical Research}, publisher={American Chemical Society (ACS)}, author={Oliynyk, Anton O. and Mar, Arthur}, year={2017}, month=dec, pages={59–68} }

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