| High-entropy ceramics C Oses, C Toher, S Curtarolo Nature Reviews Materials 5 (4), 295-309, 2020 | 1403 | 2020 |
| High-entropy high-hardness metal carbides discovered by entropy descriptors P Sarker, T Harrington, C Toher, C Oses, M Samiee, JP Maria, ... Nature communications 9 (1), 4980, 2018 | 869 | 2018 |
| Universal fragment descriptors for predicting properties of inorganic crystals O Isayev, C Oses, C Toher, E Gossett, S Curtarolo, A Tropsha Nature communications 8 (1), 15679, 2017 | 649 | 2017 |
| Machine learning modeling of superconducting critical temperature V Stanev, C Oses, AG Kusne, E Rodriguez, J Paglione, S Curtarolo, ... npj Computational Materials 4 (1), 29, 2018 | 448 | 2018 |
| The AFLOW standard for high-throughput materials science calculations CE Calderon, JJ Plata, C Toher, C Oses, O Levy, M Fornari, A Natan, ... Computational Materials Science 108, 233-238, 2015 | 322 | 2015 |
| Materials cartography: representing and mining materials space using structural and electronic fingerprints O Isayev, D Fourches, EN Muratov, C Oses, K Rasch, A Tropsha, ... Chemistry of Materials 27 (3), 735-743, 2015 | 321 | 2015 |
| On-the-fly closed-loop materials discovery via Bayesian active learning AG Kusne, H Yu, C Wu, H Zhang, J Hattrick-Simpers, B DeCost, S Sarker, ... Nature communications 11 (1), 5966, 2020 | 310 | 2020 |
| Accelerated discovery of new magnets in the Heusler alloy family S Sanvito, C Oses, J Xue, A Tiwari, M Zic, T Archer, P Tozman, ... Science advances 3 (4), e1602241, 2017 | 267 | 2017 |
| Discovery of high-entropy ceramics via machine learning K Kaufmann, D Maryanovsky, WM Mellor, C Zhu, AS Rosengarten, ... Npj Computational Materials 6 (1), 42, 2020 | 201 | 2020 |
| High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites A van Roekeghem, J Carrete, C Oses, S Curtarolo, N Mingo Physical Review X 6 (4), 041061, 2016 | 158 | 2016 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 137 | 2022 |
| AFLOW-ML: A RESTful API for machine-learning predictions of materials properties E Gossett, C Toher, C Oses, O Isayev, F Legrain, F Rose, E Zurek, ... Computational Materials Science 152, 134-145, 2018 | 104 | 2018 |
| Modeling off-stoichiometry materials with a high-throughput ab-initio approach K Yang, C Oses, S Curtarolo Chemistry of Materials 28 (18), 6484-6492, 2016 | 104 | 2016 |
| Predicting superhard materials via a machine learning informed evolutionary structure search P Avery, X Wang, C Oses, E Gossett, DM Proserpio, C Toher, S Curtarolo, ... npj Computational Materials 5 (1), 1-11, 2019 | 102 | 2019 |
| OPTIMADE, an API for exchanging materials data CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ... Scientific data 8 (1), 217, 2021 | 97 | 2021 |
| Unavoidable disorder and entropy in multi-component systems C Toher, C Oses, D Hicks, S Curtarolo Npj Computational Materials 5 (1), 69, 2019 | 95 | 2019 |
| AFLOW-CHULL: cloud-oriented platform for autonomous phase stability analysis C Oses, E Gossett, D Hicks, F Rose, MJ Mehl, E Perim, I Takeuchi, ... Journal of chemical information and modeling 58 (12), 2477-2490, 2018 | 95 | 2018 |
| A computational high-throughput search for new ternary superalloys C Nyshadham, C Oses, JE Hansen, I Takeuchi, S Curtarolo, GLW Hart Acta Materialia 122, 438-447, 2017 | 89 | 2017 |
| Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids C Toher, C Oses, JJ Plata, D Hicks, F Rose, O Levy, M de Jong, M Asta, ... Physical Review Materials 1 (1), 015401, 2017 | 76 | 2017 |
| AFLUX: The LUX materials search API for the AFLOW data repositories F Rose, C Toher, E Gossett, C Oses, MB Nardelli, M Fornari, S Curtarolo Computational Materials Science 137, 362-370, 2017 | 72 | 2017 |
Curtarolo, StefanoDuke UniversityVerified email at nietzsche.mems.duke.edu
