Research Group:
Artificial intelligence-assisted discovery of thermoelectric materials


  • Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
    L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, L. M. Ghiringhelli. submitted to Phys. Rev. Lett.
  • Interpretable Machine Learning for Materials Design
    J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, R. Goodall, and T. Bazhirov. submitted to Mach. Learn.: Sci. Technol.
  • SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach
    T.A.R. Purcell, M. Scheffler, C. Carbogno, and L. M. Ghiringhelli J. Open Source Softw. 7, 3960 (2022)
  • OPTIMADE, an API for exchanging materials data
    C. W. Andersen, et al. Sci. Data. 8, 271 (2021)
  • Anharmonicity Measure for Materials
    F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno Phys. Rev. Mater. 4, 083809 (2020)
  • FHI-vibes: Ab Initio Vibrational Simulations
    F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno J. Open Source Softw. 5, 2671 (2020)
  • Parametrically Constrained Geometry Relaxations for High-Throughput Materials Science
    M.-O. Lenz, T. A. R. Purcell, D. Hicks, S. Curtarolo, M. Scheffler, and C. Carbogno, npj Comput. Mater. 5, 123 (2019)


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