Corey Oses

Materials Science, Duke University
email
corey.oses@duke.edu
phone
(W) +1 (919) 684 1553
CV
pdf
Google Scholar
link · citations: 2437 (YTD 116) · h-index: 20

Work Experience

Postdoctoral Fellow
2018–present
Duke University
Supervisor: S. Curtarolo
Internship
Summer 2013
Cornell High Energy Synchrotron Source (BioSAXS on F2 and G Beamlines)
Supervisors: R. E. Gillilan & E. Fontes
Internship
Summer 2012
Cornell High Energy Synchrotron Source (Capillary Optics Group)
Supervisors: R. Huang & E. Fontes

Education

Ph.D.
2013–2018
Duke University

Department: Mechanical Engineering and Materials Science

Thesis: Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery

DukeSpace: link

Advisor: S. Curtarolo

B.Sc.
2009–2013
Cornell University

Department: Applied and Engineering Physics

Thesis: Plume Propagation Simulation for Pulsed Laser Deposition

Advisor: J. Brock

Journal Publications

2022

Under Review
33

High-entropy ceramics: propelling applications through disorder

Authors: C. Toher, C. Oses, M. Esters, D. Hicks, G. N. Kotsonis, C. M. Rost, D. W. Brenner, J.-P. Maria & S. Curtarolo

arXiv: arxiv:2111.11519

Frontiers in Physics

32

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Front. Phys. in press (2022)

Authors: A. G. Kusne, I. Takeuchi, B. DeCost, A. McDannald, C. Oses, S. Curtarolo, C. Toher & A. Mehta

arXiv: arxiv:2111.07478

2021

Nature Communications

31

Settling the matter of the role of vibrations in the stability of high-entropy carbides

Nat. Commun. 12, 5747 (2021)

Authors: M. Esters, C. Oses, D. Hicks, M. J. Mehl, M. Jahnátek, M. D. Hossain, J.-P. Maria, D. W. Brenner, C. Toher & S. Curtarolo

  • This paper was selected for Editor's Highlight by Springer Nature (2021).
DOI: 10.1038/s41467-021-25979-5

Advanced Materials

30

Entropy Landscaping of High-Entropy Carbides

Adv. Mater. 33(42), 2102904 (2021)

Authors: M. D. Hossain, T. Borman, C. Oses, M. Esters, C. Toher, L. Feng, A. Kumar, W. G. Fahrenholtz, S. Curtarolo, D. W. Brenner, J. M. LeBeau & J.-P. Maria

DOI: 10.1002/adma.202102904

Scientific Data

29

OPTIMADE: an API for exchanging materials data

Sci. Data 8, 217 (2021)

Authors: C. W. Andersen†, R. Armiento†, E. Blokhin†, G. J. Conduit†, S. Dwaraknath†, M. L. Evans†, Á. Fekete†, A. Gopakumar†, S. Gražulis†, A. Merkys†, F. Mohamed†, C. Oses†, G. Pizzi†, G.-M. Rignanese†, M. Scheidgen†, L. Talirz†, C. Toher†, D. Winston†, R. Aversa, K. Choudhary, P. Colinet, S. Curtarolo, D. Di Stefano, C. Draxl, S. Er, M. Esters, M. Fornari, M. Giantomassi, M. Govoni, G. Hautier, V. Hegde, M. K. Horton, P. Huck, G. Huhs, J. Hummelshøj, A. Kariryaa, B. Kozinsky, S. Kumbhar, M. Liu, N. Marzari, A. J. Morris, A. Mostofi, K. A. Persson, G. Petretto, T. Purcell, F. Ricci, F. Rose, M. Scheffler, D. Speckhard, M. Uhrin, A. Vaitkus, P. Villars, D. Waroquiers, C. Wolverton, M. Wu & X. Yang

† contributed equally

DOI: 10.1038/s41597-021-00974-z

Physical Review Materials

28

Automated coordination corrected enthalpies with AFLOW-CCE

Phys. Rev. Mater. 5, 043803 (2021)

Authors: R. Friedrich, M. Esters, C. Oses, S. Ki, M. J. Brenner, D. Hicks, M. J. Mehl, C. Toher & S. Curtarolo

DOI: 10.1103/PhysRevMaterials.5.043803

Computational Materials Science

27

The AFLOW Library of Crystallographic Prototypes: Part 3

Comput. Mater. Sci. 199, 110450 (2021)

Authors: D. Hicks, M. J. Mehl, M. Esters, C. Oses, O. Levy, G. L. Hart, C. Toher & S. Curtarolo

DOI: 10.1016/j.commatsci.2021.110450

Physical Review Materials

26

Tin-pest problem as a test of density functionals using high-throughput calculations

Phys. Rev. Mater. 5, 083608 (2021)

Authors: M. J. Mehl, M. Ronquillo, D. Hicks, M. Esters, C. Oses, R. Friedrich, A. Smolyanyuk, E. Gossett, D. Finkenstadt & S. Curtarolo

DOI: 10.1103/PhysRevMaterials.5.083608

Acta Materialia

25

Carbon Stoichiometry and Mechanical Properties of High Entropy Carbides

Acta Mater. 215, 117051 (2021)

Authors: M. D. Hossain†, T. Borman†, A. Kumar, X. Chen, A. Khosravani, S. R. Kalidindi, E. A. Paisley, M. Esters, C. Oses, C. Toher, S. Curtarolo, J. M. LeBeau, D. W. Brenner & J.-P. Maria

† contributed equally

DOI: 10.1016/j.actamat.2021.117051

2020

Nature Communications

24

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning

Nat. Commun. 11, 5966 (2020)

Authors: A. G. Kusne†, H. Yu†, C. Wu, H. Zhang, J. Hattrick-Simpers, B. DeCost, S. Sarker, C. Oses, C. Toher, S. Curtarolo, A. V. Davydov, R. Agarwal, L. A. Bendersky, M. Li, A. Mehta & I. Takeuchi

† contributed equally

DOI: 10.1038/s41467-020-19597-w

npj Computational Materials

23

Discovery of novel high-entropy ceramics via machine learning

npj Comput. Mater. 6, 42 (2020)

Authors: K. Kaufmann, D. Maryanovsky, W. M. Mellor, C. Zhu, A. S. Rosengarten, T. J. Harrington, C. Oses, C. Toher, S. Curtarolo & K. S. Vecchio

DOI: 10.1038/s41524-020-0317-6

Nature Reviews Materials

22

High-entropy ceramics

Nat. Rev. Mater. 5, 295–309 (2020)

Authors: C. Oses, C. Toher & S. Curtarolo

  • This paper was highlighted as a "hot paper" by Web of Science (Clarivate Analytics) (November 16, 2021).
DOI: 10.1038/s41578-019-0170-8

2019

Acta Materialia

21

Metallic glasses for biodegradable implants

Acta Mater. 176, 297–305 (2019)

Authors: D. C. Ford, D. Hicks, C. Oses, C. Toher & S. Curtarolo

DOI: 10.1016/j.actamat.2019.07.008

npj Computational Materials

20

Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search

npj Comput. Mater. 5, 89 (2019)

Authors: P. Avery, X. Wang, C. Oses, E. Gossett, D. M. Proserpio, C. Toher, S. Curtarolo & E. Zurek

DOI: 10.1038/s41524-019-0226-8

npj Computational Materials

19

Unavoidable disorder and entropy in multi-component systems

npj Comput. Mater. 5, 69 (2019)

Authors: C. Toher, C. Oses, D. Hicks & S. Curtarolo

DOI: 10.1038/s41524-019-0206-z

npj Computational Materials

18

Coordination corrected ab initio formation enthalpies

npj Comput. Mater. 5, 59 (2019)

Authors: R. Friedrich, D. Usanmaz, C. Oses, A. R. Supka, M. Fornari, M. Buongiorno Nardelli, C. Toher & S. Curtarolo

DOI: 10.1038/s41524-019-0192-1

Physical Review Materials

17

AFLOW-QHA3P: Robust and automated method to compute thermodynamic properties of solids

Phys. Rev. Mater. 3, 073801 (2019)

Authors: P. Nath, D. Usanmaz, D. Hicks, C. Oses, M. Fornari, M. Buongiorno Nardelli, C. Toher & S. Curtarolo

DOI: 10.1103/PhysRevMaterials.3.073801

2018

Journal of Chemical Information and Modeling

16

AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis

J. Chem. Inf. Model. 58(12), 2477–2490 (2018)

Authors: C. Oses, E. Gossett, D. Hicks, F. Rose, M. J. Mehl, E. Perim, I. Takeuchi, S. Sanvito, M. Scheffler, Y. Lederer, O. Levy, C. Toher & S. Curtarolo

DOI: 10.1021/acs.jcim.8b00393

MRS Bulletin

15

Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

MRS Bull. 43(9), 670–675 (2018)

Authors: C. Oses, C. Toher & S. Curtarolo

DOI: 10.1557/mrs.2018.207

Nature Communications

14

High-entropy high-hardness metal carbides discovered by entropy descriptors

Nat. Commun. 9, 4980 (2018)

Authors: P. Sarker†, T. J. Harrington†, C. Toher, C. Oses, M. Samiee, J.-P. Maria, D. W. Brenner, K. S. Vecchio & S. Curtarolo

† contributed equally

DOI: 10.1038/s41467-018-07160-7

npj Computational Materials

13

Machine learning modeling of superconducting critical temperature

npj Comput. Mater. 4, 29 (2018)

Authors: V. Stanev, C. Oses, A. G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo & I. Takeuchi

DOI: 10.1038/s41524-018-0085-8

Computational Materials Science

12

AFLOW-ML: A RESTful API for machine-learning prediction of materials properties

Comput. Mater. Sci. 152, 134–145 (2018)

Authors: E. Gossett, C. Toher, C. Oses, O. Isayev, F. Legrain, F. Rose, E. Zurek, J. Carrete, N. Mingo, A. Tropsha & S. Curtarolo

  • This paper was selected for Editor's Choice by Elsevier (2018).
DOI: 10.1016/j.commatsci.2018.03.075

Acta Crystallographica Section A

11

AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals

Acta Cryst. A 74, 184–203 (2018)

Authors: D. Hicks, C. Oses, E. Gossett, G. Gomez, R. H. Taylor, C. Toher, M. J. Mehl, O. Levy & S. Curtarolo

DOI: 10.1107/S2053273318003066

2017

Inorganic Chemistry

10

The structure and composition statistics of 6A binary and ternary structures

Inorg. Chem. 57(2), 653–667 (2017)

Authors: A. Hever, C. Oses, S. Curtarolo, O. Levy & A. Natan

DOI: 10.1021/acs.inorgchem.7b02462

Computational Materials Science

9

AFLUX: The LUX materials search API for the AFLOW data repositories

Comput. Mater. Sci. 137, 362–370 (2017)

Authors: F. Rose, C. Toher, E. Gossett, C. Oses, M. Buongiorno Nardelli, M. Fornari & S. Curtarolo

  • This paper was selected for Editor's Choice by Elsevier (2017).
DOI: 10.1016/j.commatsci.2017.04.036

Nature Communications

8

Universal Fragment Descriptors for Predicting Properties of Inorganic Crystals

Nat. Commun. 8, 15679 (2017)

Authors: O. Isayev†, C. Oses†, C. Toher, E. Gossett, S. Curtarolo & A. Tropsha

† contributed equally

DOI: 10.1038/ncomms15679

Physical Review Materials

7

Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screening thermomechanical properties of solids

Phys. Rev. Mater. 1, 015401 (2017)

Authors: C. Toher, C. Oses, J. J. Plata, D. Hicks, F. Rose, O. Levy, M. de Jong, M. Asta, M. Fornari, M. Buongiorno Nardelli & S. Curtarolo

DOI: 10.1103/PhysRevMaterials.1.015401

Acta Materialia

6

A Computational High-Throughput Search for New Ternary Superalloys

Acta Mater. 122, 438–447 (2017)

Authors: C. Nyshadham, C. Oses, J. E. Hansen, I. Takeuchi, S. Curtarolo & G. L. Hart

DOI: 10.1016/j.actamat.2016.09.017

Science Advances

5

Accelerated Discovery of New Magnets in the Heusler Alloy Family

Sci. Adv. 3(4), e1602241 (2017)

Authors: S. Sanvito, C. Oses, J. Xue, A. Tiwari, M. Žic, T. Archer, P. Tozman, M. Venkatesan, J. D. Coey & S. Curtarolo

DOI: 10.1126/sciadv.1602241

2016

Physical Review X

4

High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites

Phys. Rev. X 6(4), 041061 (2016)

Authors: A. van Roekeghem, J. Carrete, C. Oses, S. Curtarolo & N. Mingo

DOI: 10.1103/PhysRevX.6.041061

Chemistry of Materials

3

Modeling Off-Stoichiometry Materials with a High-Throughput Ab-Initio Approach

Chem. Mater. 28(18), 6484–6492 (2016)

Authors: K. Yang, C. Oses & S. Curtarolo

DOI: 10.1021/acs.chemmater.6b01449

2015

Computational Materials Science

2

The AFLOW Standard for High-Throughput Materials Science Calculations

Comput. Mater. Sci. 108A, 233–238 (2015)

Authors: C. E. Calderon, J. J. Plata, C. Toher, C. Oses, O. Levy, M. Fornari, A. Natan, M. J. Mehl, G. L. Hart, M. Buongiorno Nardelli & S. Curtarolo

  • This paper was selected for Editor's Choice by Elsevier (2015).
DOI: 10.1016/j.commatsci.2015.07.019

Chemistry of Materials

1

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

Chem. Mater. 27(3), 735–743 (2015)

Authors: O. Isayev, D. Fourches, E. N. Muratov, C. Oses, K. M. Rasch, A. Tropsha & S. Curtarolo

  • This paper was selected for Editor's Choice by the American Chemical Society (2015).
DOI: 10.1021/cm503507h

Book Publications

2019

Book Chapter
3

Automated computation of materials properties

Materials Informatics: Methods, Tools and Applications, Ch. 7

Authors: C. Toher, C. Oses & S. Curtarolo

URL: wiley.com/en-us/Materials+Informatics%3A+Methods%2C+Tools%2C+and+Applications-p-9783527802272

2018

Book Chapter
2

Machine learning and high-throughput approaches to magnetism

Handbook of Materials Modeling. Volume 2 Applications: Current and Emerging Materials

Authors: S. Sanvito, M. Žic, J. Nelson, T. Archer, C. Oses & S. Curtarolo

DOI: 10.1007/978-3-319-50257-1_108-1
Book Chapter
1

The AFLOW Fleet for Materials Discovery

Handbook of Materials Modeling. Volume 1 Methods: Theory and Modeling

Authors: C. Toher, C. Oses, D. Hicks, E. Gossett, F. Rose, P. Nath, D. Usanmaz, D. C. Ford, E. Perim, C. E. Calderon, J. J. Plata, Y. Lederer, M. Jahnátek, W. Setyawan, S. Wang, J. Xue, K. M. Rasch, R. V. Chepulskii, R. H. Taylor, G. Gomez, H. Shi, A. R. Supka, R. Al Rahal Al Orabi, P. Gopal, F. T. Cerasoli, L. Liyanage, H. Wang, I. Siloi, L. A. Agapito, C. Nyshadham, G. L. Hart, J. Carrete, F. Legrain, N. Mingo, E. Zurek, O. Isayev, A. Tropsha, S. Sanvito, R. M. Hanson, I. Takeuchi, M. J. Mehl, A. N. Kolmogorov, K. Yang, P. D'Amico, A. Calzolari, M. Costa, R. De Gennaro, M. Buongiorno Nardelli, M. Fornari, O. Levy & S. Curtarolo

DOI: 10.1007/978-3-319-42913-7_63-1

Teaching Experience

Co-Instructor
Spring 2021

ME 555: Applications of Artificial Intelligence in Materials, Duke University Department of Mechanical Engineering and Materials Science

Teaching Assistant
Spring 2020

ME 555: Computational Materials Science by Examples and Applications, Duke University Department of Mechanical Engineering and Materials Science

Teaching Assistant
Fall 2014–Spring 2015

ME 221: Structure and Properties of Solids, Duke University Department of Mechanical Engineering and Materials Science

  • Best Teaching Assistant Award, August 14, 2015

Workshops

Organizer And Presenter
2021

AFLOW School: Integrated infrastructure for computational materials discovery

Co-Organizers: C. Toher, D. Hicks, M. Esters, E. Gossett, A. Smolyanyuk, M. J. Brenner, R. Friedrich & S. Curtarolo

  1. Organizer and presenter at the Technische Universität (TU) Dresden and Helmholtz-Zentrum Dresden-Rossendorf — September 6–10, 2021.
  1. Organizer and presenter at the University of Virginia, Charlottesville, Virginia — August 17, 2021.
  1. Presenter for the Machine Learning for Materials Research Bootcamp & Workshop on Machine Learning Quantum Materials of NIST/Moore Foundation/University of Maryland, Institute for Bioscience & Biotechnology Research in Gaithersburg, Maryland — July 29, 2021.
  1. Organizer and presenter at Texas A&M University, College Station, Texas — July 12–15, 2021.

  1. Session Chair for the Virtual Spring Meeting Tutorial of the Materials Research Society — April 17, 2021.

  1. Presenter for the Materials 4.0 Summer School 2020 at the Dresden Center for Computational Materials Science (DCMS), Technische Universität (TU) Dresden — August 18, 2020.
  1. Presenter for the Machine Learning for Materials Research Bootcamp & Workshop on Machine Learning Quantum Materials of NIST/Moore Foundation/University of Maryland, Institute for Bioscience & Biotechnology Research in Gaithersburg, Maryland — July 23, 2020.
  1. Organizer and presenter at Texas A&M University, College Station, Texas — June 16–18, 2020.

  1. Presenter for the Machine Learning for Materials Research Bootcamp & Workshop on Machine Learning Quantum Materials of NIST/Moore Foundation/University of Maryland, Institute for Bioscience & Biotechnology Research in Gaithersburg, Maryland — August 05, 2019.

  1. Organizer and presenter at the University of Pennsylvania, Philadelphia, Pennsylvania — May 03, 2019.

  1. Organizer and presenter at the North Carolina State University, Raleigh, North Carolina — March 12, 2019.

  1. Organizer and presenter at Carnegie Mellon University, Pittsburgh, Pennsylvania — January 21, 2019.

  1. Presenter for the Machine Learning for Materials Research Bootcamp & Workshop on Machine Learning Quantum Materials of NIST/Moore Foundation/University of Maryland, Institute for Bioscience & Biotechnology Research in Gaithersburg, Maryland — August 02, 2018.

Press and News Releases

White House Office of Science & Technology Policy
November 18, 2021
"Featured Vignette in the November 2021 Materials Genome Initiative Strategic Plan (page 9)"
mgi.gov/sites/default/files/documents/MGI-2021-Strategic-Plan.pdf
University of Buffalo
September 2019
"Scientists predict new forms of superhard carbon"
buffalo.edu/ubnow/stories/2019/09/zurek-superhard-carbon.html
Duke University Pratt School of Engineering
November 2018
"Disordered Materials Could Be Hardest, Most Heat-Tolerant Ever"
pratt.duke.edu/about/news/chaotic-carbides
MRS Bulletin
August 2017
"Universal fragment descriptor predicts materials properties"
cambridge.org/core/journals/mrs-bulletin/news/universal-fragment-descriptor-predicts-materials-properties
UNC Eshelman School of Pharmacy
June 2017
"Breakthrough Tool Predicts Properties of Theoretical Materials, Finds New Uses for Current Ones"
pharmacy.unc.edu/news/2017/06/06/breakthrough-tool-predicts-properties-theoretical-materials-finds-new-uses-current-ones/
Duke University Pratt School of Engineering
April 2017
"Computers Create Recipe for Two New Magnetic Materials"
pratt.duke.edu/about/news/predicting-magnets
MRS Bulletin
April 2015
"Materials fingerprints identified for informatics"
doi.org/10.1557/mrs.2015.76
Computational Chemistry Highlights
January 2015
"Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints"
  • "This paper is a tour de force for computational materials science" — Prof. Aspuru-Guzik.
compchemhighlights.org/2015/01/materials-cartography-representing-and.html
Duke University Research
January 2015
"Molecular Tornado"
research.duke.edu/molecular-tornado
Duke University Graduate School
October 2014
"Competing for NSF Fellowships: Advice from a Current Fellow"
gradschool.duke.edu/professional-development/blog/competing-nsf-fellowships-advice-current-fellow
ERN Conference 2013
February 2013
"2013 Oral and Poster Presentation Award Winners"
new.emerging-researchers.org/2013-oral-and-poster-presentation-winners

Honors and Awards

Publication Award
November 16, 2021
"Hot paper", Publication in Nat. Rev. Mater., Web of Science (Clarivate Analytics)
  • Published in the past two years and received enough citations in July/August 2021 to place it in the top 0.1% of papers in the academic field of Materials Science
Publication Award
2021
Editor's Highlight, Publication in Nat. Commun., Springer Nature
Publication Award
2018
Editor's Choice, Publication in Comput. Mater. Sci., Elsevier
Publication Award
2017
Editor's Choice, Publication in Comput. Mater. Sci., Elsevier
Award
August 14, 2015
Best Teaching Assistant Award (ME 221), Duke University Department of Mechanical Engineering and Materials Science
Publication Award
2015
Editor's Choice, Publication in Comput. Mater. Sci., Elsevier
Publication Award
2015
Editor's Choice, Publication in Chem. Mater., American Chemical Society
Fellowship
2013–2016
Graduate Research Fellowship, National Science Foundation
Award
August 22, 2013
Best Presentation Award at the MEMS Departmental Retreat, Duke University Department of Mechanical Engineering and Materials Science
Award
March 02, 2013
First Place in Nanoscience and Physics Research Presentation, NSF / AAAS / EHR Emerging Researchers National Conference
Scholarship
2011–2013
Shell Incentive Fund Scholarship
Scholarship
2010 & 2011
Xerox Corporation Scholarship
Scholarship
2010 & 2011
Intel Academic Award
Grant
June 18, 2010
Cornell University Unmanned Air Systems Team awarded $1,000 grant, AUVSI Student Unmanned Aerial Systems Competition
Scholarship
2009–2013
Meinig Family Cornell National Scholars
  • Awarded by Peter Meinig (Past Chairman of the Board of Trustees at Cornell University)

Certifications

Participant
June 8–12, 2020
CECAM (Centre Européen de Calcul Atomique et Moléculaire) Open Databases Integration for Materials Design (OPTiMaDe) Workshop at the École polytechnique fédérale de Lausanne (EPFL)
Participant
June 11–14, 2019
CECAM (Centre Européen de Calcul Atomique et Moléculaire) Open Databases Integration for Materials Design (OPTiMaDe) Workshop at the École polytechnique fédérale de Lausanne (EPFL)
Graduate
June 25–29, 2018
Machine Learning Summer School (MLSS) at Duke University
Participant
June 11–15, 2018
CECAM (Centre Européen de Calcul Atomique et Moléculaire) Open Databases Integration for Materials Design (OPTiMaDe) Workshop at the École polytechnique fédérale de Lausanne (EPFL)
Graduate
January 7–16, 2015
Machine Learning Summer School (MLSS) at the University of Texas at Austin
Graduate
May 22–27, 2011
The LeaderShape Institute at Cornell University
Technician License
July 29, 2010
American Radio Relay League (ARRL) in Roselle, New Jersey