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

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

High-throughput Thermodynamic Search of Novel Phonon-mediated Covalent Metal Superconductors awarded by Office of Naval Research (Principal Investigator). 2010 to 2011


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

Ouyang, R., et al. “SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates.” Physical Review Materials, vol. 2, no. 8, Aug. 2018. Scopus, doi:10.1103/PhysRevMaterials.2.083802. Full Text

Hicks, David, et al. “AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals.Acta Crystallographica. Section A, Foundations and Advances, vol. 74, no. Pt 3, May 2018, pp. 184–203. Epmc, doi:10.1107/s2053273318003066. Full Text

Usanmaz, D., et al. “Spinodal Superlattices of Topological Insulators.” Chemistry of Materials, vol. 30, no. 7, Apr. 2018, pp. 2331–40. Scopus, doi:10.1021/acs.chemmater.7b05299. Full Text

Buongiorno Nardelli, M., et al. “PAOFLOW: A utility to construct and operate on ab initio Hamiltonians from the projections of electronic wavefunctions on atomic orbital bases, including characterization of topological materials.” Computational Materials Science, vol. 143, Feb. 2018, pp. 462–72. Scopus, doi:10.1016/j.commatsci.2017.11.034. Full Text