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

Topological decompositions and spectral sampling algorithms for element substitution in critical technologies awarded by Office of Naval Research (Principal Investigator). 2013 to 2019

DMREF: GOALI: Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses awarded by National Science Foundation (Principal Investigator). 2014 to 2018

Reciprocating materials design with the repository awarded by Office of Naval Research (Principal Investigator). 2014 to 2018

Mineralogy Genome Project: extending the repository to geophysical and environmental materials awarded by Office of Naval Research (Principal Investigator). 2015 to 2017

Materials Project for Functional Electronic Materials Design awarded by Ernest Orlando Lawrence Berkeley National Laboratory (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


Siloi, I., et al. “Thermoelectric Properties of Minerals with the Mawsonite Structure.” Acs Applied Energy Materials, vol. 2, no. 11, Nov. 2019, pp. 8068–78. Scopus, doi:10.1021/acsaem.9b01564. Full Text

Gusmão, Marta S. S., et al. “Mechanical Properties of Chemically Modified Clay.Scientific Reports, vol. 9, no. 1, Sept. 2019, p. 13698. Epmc, doi:10.1038/s41598-019-49972-7. Full Text

Ford, D. C., et al. “Metallic glasses for biodegradable implants.” Acta Materialia, vol. 176, Sept. 2019, pp. 297–305. Scopus, doi:10.1016/j.actamat.2019.07.008. Full Text

Nath, P., et al. “AFLOW-QHA3P: Robust and automated method to compute thermodynamic properties of solids.” Physical Review Materials, vol. 3, no. 7, July 2019. Scopus, doi:10.1103/PhysRevMaterials.3.073801. Full Text

Hicks, D., et al. “The AFLOW Library of Crystallographic Prototypes: Part 2.” Computational Materials Science, vol. 161, Apr. 2019, pp. S1–1011. Scopus, doi:10.1016/j.commatsci.2018.10.043. Full Text

Avery, P., et al. “XTALOPT Version r12: An open-source evolutionary algorithm for crystal structure prediction.” Computer Physics Communications, vol. 237, Apr. 2019, pp. 274–75. Scopus, doi:10.1016/j.cpc.2018.11.016. Full Text

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