Corey Oses

Materials Science, Duke University

Personal Information

email
corey.oses@duke.edu
phone
(W) +1 (919) 684 1553
CV
pdf
Google Scholar
link

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

ProQuest: 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

Press and News Releases

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
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

Workshops

Organizer And Presenter
2020

AFLOW School: Integrated infrastructure for computational materials discovery

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

  1. Presenter at the Dresden Center for Computational Materials Science (DCMS) Materials 4.0 Summer School 2020, Technische Universität Dresden — August 18, 2020.
  1. Presenter at the NIST/Moore Foundation/University of Maryland Machine Learning for Materials Research Bootcamp 2019 & Workshop on Machine Learning Quantum Materials, Institute for Bioscience & Biotechnology Research in Gaithersburg, Maryland — July 23, 2020.
  1. Organizer and presenter at the Texas A&M University AFLOW Multi-Day Workshop, College Station, Texas — June 16–18, 2020.

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

  1. Organizer and presenter at the University of Pennsylvania AFLOW Full-Day Workshop, Philadelphia, Pennsylvania — May 03, 2019.

  1. Organizer and presenter at the North Carolina State University AFLOW Full-Day Workshop, Raleigh, North Carolina — March 12, 2019.

  1. Organizer and presenter at the Carnegie Mellon University AFLOW Full-Day Workshop, Pittsburgh, Pennsylvania — January 21, 2019.

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

Journal Publications

2020

Under Review
27

The Tin Pest Problem as a Test of Density Functionals Using High-Throughput Calculations

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

arXiv: arxiv:2010.07168
Under Review
26

Carbon Stoichiometry and Mechanical Properties of High Entropy Carbide

Authors: M. D. Hossain†, T. Borman†, F. 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

Under Review
25

Fermi energy engineering of enhanced toughness in high entropy carbides

Authors: T. J. Harrington†, C. Oses†, C. Toher, W. M. Mellor, K. Kaufmann, J. Gild, A. Wright, J. Luo, S. Curtarolo & K. S. Vecchio

† contributed equally

Nature Communications

24

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

Nat. Commun. in press (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. Davidov, R. Agarwal, L. Bendersky, M. Li, A. Mehta & I. Takeuchi

† contributed equally

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

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

Novel 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.
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.
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.
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.
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

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

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