Stefano Curtarolo

Stefano Curtarolo

Professor in the Department of Mechanical Engineering and Materials Science

Professor in the Department of Physics (Secondary)

Professor in the Department of Electrical and Computer Engineering (Secondary)

Faculty Network Member of The Energy Initiative

Office Location: 
301 Hudson Hall, Box 90300, Durham, NC 27708
Front Office Address: 
301 Hudson Hall, Box 90300, Durham, NC 27708-0300
(919) 660-5506



  • Nanoscale Science of Energy
  • Computational materials science
  • Nanotube growth characterization
  • Alloy theory
  • Superlubricity on quasicrystals
  • Superconductivity in Metal borides
  • Genetic Approaches to QM Predictions of Materials Structures
  • Materials for Nuclear Detection

The research is multidisciplinary and makes use of state of the art techniques from fields like materials science, chemistry, physics, quantum mechanics, mathematics and computer science.

Education & Training

  • Ph.D., Massachusetts Institute of Technology 2003

  • M.S., Pennsylvania State University 1999

  • M.S., University of Padua (Italy) 1995

Selected Grants

Materials Project for Functional Electronic Materials Design awarded by (Principal Investigator). 2013 to 2017

Integration of the experimental superconductor database with AFLOWLIB awarded by University of Maryland (Principal Investigator). 2016 to 2017

Expanding AFLOW Visualization using Jmol V2 awarded by Office of Naval Research (Principal Investigator). 2016 to 2017

Enhancing AFLOW Visualization Using Jmol awarded by Office of Naval Research (Principal Investigator). 2015 to 2016

Development of Materials Fingerprints for Efficient Database Mining and Predictive QSPR Modeling awarded by Office of Naval Research (Principal Investigator). 2012 to 2016

PECASE: Fundamental Thermodynamic Problems at the Nanoscale, order-disorder transitions in precipitates and alloyed awarded by Office of Naval Research (Principal Investigator). 2009 to 2015

Computer Aided Materials Design Of Superconductors by High-Throughput Thermodynamics awarded by Office of Naval Research (Principal Investigator). 2010 to 2015

Material Genome: Duke-NIST Initiative awarded by National Institute of Standards and Technology (Principal Investigator). 2012 to 2014

CAREER: Genetic Approaches to Quantum Mechanics Predictions of Materials Structures awarded by National Science Foundation (Principal Investigator). 2007 to 2013

Computational Equipment for the "Materials Genome Initiative for Global Competitiveness" at Duke University awarded by Office of Naval Research (Principal Investigator). 2012 to 2013


Harrington, T. J., et al. “Phase stability and mechanical properties of novel high entropy transition metal carbides.” Acta Materialia, vol. 166, Mar. 2019, pp. 271–80. Scopus, doi:10.1016/j.actamat.2018.12.054. Full Text

Sławińska, J., et al. “Giant spin Hall effect in two-dimensional monochalcogenides.” 2d Materials, vol. 6, no. 2, Feb. 2019. Scopus, doi:10.1088/2053-1583/ab0146. Full Text

Alberi, K., et al. “The 2019 materials by design roadmap.” Journal of Physics D: Applied Physics, vol. 52, no. 1, Jan. 2019. Scopus, doi:10.1088/1361-6463/aad926. Full Text

Stanev, V., et al. “Machine learning modeling of superconducting critical temperature.” Npj Computational Materials, vol. 4, no. 1, Dec. 2018. Scopus, doi:10.1038/s41524-018-0085-8. Full Text

Oses, Corey, et al. “AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis.Journal of Chemical Information and Modeling, vol. 58, no. 12, Dec. 2018, pp. 2477–90. Epmc, doi:10.1021/acs.jcim.8b00393. Full Text

Legrain, Fleur, et al. “Vibrational Properties of Metastable Polymorph Structures by Machine Learning.Journal of Chemical Information and Modeling, vol. 58, no. 12, Dec. 2018, pp. 2460–66. Epmc, doi:10.1021/acs.jcim.8b00279. Full Text

Sarker, Pranab, et al. “High-entropy high-hardness metal carbides discovered by entropy descriptors.Nature Communications, vol. 9, no. 1, Nov. 2018, p. 4980. Epmc, doi:10.1038/s41467-018-07160-7. Full Text

Lederer, Y., et al. “The search for high entropy alloys: A high-throughput ab-initio approach.” Acta Materialia, vol. 159, Oct. 2018, pp. 364–83. Scopus, doi:10.1016/j.actamat.2018.07.042. Full Text

Gossett, E., et al. “AFLOW-ML: A RESTful API for machine-learning predictions of materials properties.” Computational Materials Science, vol. 152, Sept. 2018, pp. 134–45. Scopus, doi:10.1016/j.commatsci.2018.03.075. Full Text

Oses, C., et al. “Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery.” Mrs Bulletin, vol. 43, no. 9, Sept. 2018, pp. 670–75. Scopus, doi:10.1557/mrs.2018.207. Full Text