Corey Oses

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Curtarolo, StefanoDuke UniversityPatvirtintas el. paštas nietzsche.mems.duke.edu
Cormac ToherAssistant ProfessorPatvirtintas el. paštas utdallas.edu
David HicksDuke UniversityPatvirtintas el. paštas duke.edu
Michael MehlDuke UniversityPatvirtintas el. paštas duke.edu
Marco EstersDuke UniversityPatvirtintas el. paštas duke.edu
Ichiro TakeuchiUniversity of MarylandPatvirtintas el. paštas umd.edu
D W BrennerProfessor at North Carolina State UniversityPatvirtintas el. paštas ncsu.edu
Alexander TropshaProfessor at UNC-Chapel HillPatvirtintas el. paštas unc.edu
Olexandr IsayevCarl and Amy Jones Professor of Chemistry, Carnegie Mellon UniversityPatvirtintas el. paštas olexandrisayev.com
Marco Buongiorno NardelliRegents Professor of Physics and Composition, UNTPatvirtintas el. paštas unt.edu
Marco FornariDepartment of Physics, Central Michigan UniversityPatvirtintas el. paštas cmich.edu
A Gilad KusneMaterials Measurement & Science Division, NIST & Materials Science & Engineering, UMDPatvirtintas el. paštas umd.edu
Tyler HarringtonApple Inc.Patvirtintas el. paštas apple.com
Eva ZurekProfessor Department of ChemistryPatvirtintas el. paštas buffalo.edu
Frisco RoseDuke UniversityPatvirtintas el. paštas duke.edu
Jose J. PlataUniversidad de SevillaPatvirtintas el. paštas us.es
Gus L. W. HartBrigham Young University, Physics and AstronomyPatvirtintas el. paštas byu.edu
Stefano SanvitoTrinity College DublinPatvirtintas el. paštas tcd.ie
Apurva MehtaSLAC national Accelerator LabPatvirtintas el. paštas slac.stanford.edu
Brian L. DeCostNational Institute of Standards and TechnologyPatvirtintas el. paštas nist.gov

Stebėti

Corey Oses

Johns Hopkins University

Patvirtintas el. paštas jhu.edu - Pagrindinis puslapis

Materials Science


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
High-entropy ceramics C Oses, C Toher, S Curtarolo Nature Reviews Materials 5 (4), 295-309, 2020	1504	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	906	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	668	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	459	2018
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	337	2020
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	331	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	325	2015
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	276	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	211	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	159	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	152	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	110	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), 89, 2019	109	2019
OPTIMADE, an API for exchanging materials data CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ... Scientific data 8 (1), 217, 2021	108	2021
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	106	2016
Unavoidable disorder and entropy in multi-component systems C Toher, C Oses, D Hicks, S Curtarolo Npj Computational Materials 5 (1), 69, 2019	98	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	91	2018
Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery S Divilov, H Eckert, D Hicks, C Oses, C Toher, R Friedrich, M Esters, ... Nature 625 (7993), 66-73, 2024	88	2024
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	88	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	77	2017

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