| High-throughput machine-learning-driven synthesis of full-Heusler compounds AO Oliynyk, E Antono, TD Sparks, L Ghadbeigi, MW Gaultois, B Meredig, ... Chemistry of Materials 28 (20), 7324-7331, 2016 | 344 | 2016 |
| Machine learning for materials scientists: an introductory guide toward best practices AYT Wang, RJ Murdock, SK Kauwe, AO Oliynyk, A Gurlo, J Brgoch, ... Chemistry of Materials 32 (12), 4954-4965, 2020 | 306 | 2020 |
| How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics B Cao, LA Adutwum, AO Oliynyk, EJ Luber, BC Olsen, A Mar, JM Buriak ACS nano 12 (8), 7434-7444, 2018 | 284 | 2018 |
| Machine learning directed search for ultraincompressible, superhard materials A Mansouri Tehrani, AO Oliynyk, M Parry, Z Rizvi, S Couper, F Lin, ... Journal of the American Chemical Society 140 (31), 9844-9853, 2018 | 280 | 2018 |
| Identifying an efficient, thermally robust inorganic phosphor host via machine learning Y Zhuo, A Mansouri Tehrani, AO Oliynyk, AC Duke, J Brgoch Nature communications 9 (1), 4377, 2018 | 266 | 2018 |
| Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties MW Gaultois, AO Oliynyk, A Mar, TD Sparks, GJ Mulholland, B Meredig Apl Materials 4 (5), 2016 | 185 | 2016 |
| Data mining our way to the next generation of thermoelectrics TD Sparks, MW Gaultois, A Oliynyk, J Brgoch, B Meredig Scripta Materialia 111, 10-15, 2016 | 128 | 2016 |
| Discovery of intermetallic compounds from traditional to machine-learning approaches AO Oliynyk, A Mar Accounts of chemical research 51 (1), 59-68, 2018 | 124 | 2018 |
| Machine learning in materials discovery: confirmed predictions and their underlying approaches JE Saal, AO Oliynyk, B Meredig Annual Review of Materials Research 50, 49-69, 2020 | 106 | 2020 |
| Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC AO Oliynyk, LA Adutwum, BW Rudyk, H Pisavadia, S Lotfi, V Hlukhyy, ... Journal of the American Chemical Society 139 (49), 17870-17881, 2017 | 103 | 2017 |
| Classifying crystal structures of binary compounds AB through cluster resolution feature selection and support vector machine analysis AO Oliynyk, LA Adutwum, JJ Harynuk, A Mar Chemistry of Materials 28 (18), 6672-6681, 2016 | 93 | 2016 |
| Silicon nanoparticles: are they crystalline from the core to the surface? AN Thiessen, M Ha, RW Hooper, H Yu, AO Oliynyk, JGC Veinot, ... Chemistry of Materials 31 (3), 678-688, 2019 | 73 | 2019 |
| Tailorable indirect to direct band-gap double perovskites with bright white-light emission: decoding chemical structure using solid-state NMR A Karmakar, GM Bernard, A Meldrum, AO Oliynyk, VK Michaelis Journal of the American Chemical Society 142 (24), 10780-10793, 2020 | 69 | 2020 |
| Finding the next superhard material through ensemble learning Z Zhang, A Mansouri Tehrani, AO Oliynyk, B Day, J Brgoch Advanced Materials 33 (5), 2005112, 2021 | 57 | 2021 |
| Alkaline earth metal–organic frameworks with tailorable ion release: a path for supporting biomineralization MA Matlinska, M Ha, B Hughton, AO Oliynyk, AK Iyer, GM Bernard, ... ACS applied materials & interfaces 11 (36), 32739-32745, 2019 | 32 | 2019 |
| A tale of seemingly “Identical” silicon quantum dot families: structural insight into silicon quantum dot photoluminescence AN Thiessen, L Zhang, AO Oliynyk, H Yu, KM O’Connor, A Meldrum, ... Chemistry of Materials 32 (16), 6838-6846, 2020 | 31 | 2020 |
| Virtual issue on machine-learning discoveries in materials science AO Oliynyk, JM Buriak Chemistry of Materials 31 (20), 8243-8247, 2019 | 31 | 2019 |
| Half-heusler structures with full-heusler counterparts: machine-learning predictions and experimental validation AS Gzyl, AO Oliynyk, A Mar Crystal Growth & Design 20 (10), 6469-6477, 2020 | 29 | 2020 |
| Rare-earth transition-metal gallium chalcogenides RE3MGaCh7 (M= Fe, Co, Ni; Ch= S, Se) BW Rudyk, SS Stoyko, AO Oliynyk, A Mar Journal of Solid State Chemistry 210 (1), 79-88, 2014 | 28 | 2014 |
| Significant variability in the photocatalytic activity of natural titanium-containing minerals: implications for understanding and predicting atmospheric mineral dust … M Abou-Ghanem, AO Oliynyk, Z Chen, LC Matchett, DT McGrath, MJ Katz, ... Environmental Science & Technology 54 (21), 13509-13516, 2020 | 26 | 2020 |
