Corey Oses
Corey Oses
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Cited by
Cited by
High-entropy ceramics
C Oses, C Toher, S Curtarolo
Nature Reviews Materials 5 (4), 295-309, 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
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
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
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
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
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
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
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
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
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
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
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
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
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
OPTIMADE, an API for exchanging materials data
CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ...
Scientific data 8 (1), 217, 2021
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
Unavoidable disorder and entropy in multi-component systems
C Toher, C Oses, D Hicks, S Curtarolo
Npj Computational Materials 5 (1), 69, 2019
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
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
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