Stefano Curtarolo

Stefano Curtarolo

Professor in the Department of Mechanical Engineering and Materials Science

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

Overview

RESEARCH FIELDS

  • 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

The Science of Entropy Stabilized Ultra-High Temperature Materials awarded by North Carolina State University (Principal Investigator). 2015 to 2020

Automated Characterization of Chemical Bonding in Inorganic Crystals awarded by Office of Naval Research (Principal Investigator). 2018 to 2019

Materials-similarity metrics for the AFLOW data repository awarded by Office of Naval Research (Principal Investigator). 2018 to 2019

Disorder as the discovery enabler for transition-metal mixed-anion materials awarded by Office of Naval Research (Co-Principal Investigator). 2017 to 2019

Synergetic Efforts in Automatic Accelerated Materials Design awarded by Office of Naval Research (Principal Investigator). 2016 to 2019

High-throughput Prediction of High-pressure Materials Properties for the AFLOWLIB Database awarded by Office of Naval Research (Principal Investigator). 2016 to 2019

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 AFLOWLIB.org repository awarded by Office of Naval Research (Principal Investigator). 2014 to 2018

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

Pages

Hosseinian, S., et al. “The maximum edge weight clique problem: Formulations and solution approaches.” Springer Optimization and Its Applications, vol. 130, 2017, pp. 217–37. Scopus, doi:10.1007/978-3-319-68640-0_10. 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

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. 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

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

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

Pages

Isayev, Olexandr, et al. “Quantitative materials structure-property relationships (QMSPR) modeling using novel electronic and structural descriptors.” Abstracts of Papers of the American Chemical Society, vol. 248, AMER CHEMICAL SOC, 2014.

Isayev, Olexandr, et al. “Materials cartography: Navigating through chemical space using structural and electronic fingerprints.” Abstracts of Papers of the American Chemical Society, vol. 248, AMER CHEMICAL SOC, 2014.

Curtarolo, Stefano. “Distributed synergies for materials development: The aflowlib.org consortium.” Abstracts of Papers of the American Chemical Society, vol. 243, AMER CHEMICAL SOC, 2012.

Ceder, G., et al. “First principles calculated databases for the prediction of intermetallic structure..” Abstracts of Papers of the American Chemical Society, vol. 226, 2003, pp. U303–U303.