The NOMAD Laboratory

Novel Materials Discovery at the FHI Molecular Physics Department
of the Max-Planck-Gesellschaft


Dr. Thomas Purcell


Member since 08/2018
Phone: +49 30 8413 4853
Room: T 1.10
Email: purcell@fhi.mpg.de


RESEARCH GROUP: Artificial Intelligence-Assisted Discovery of Thermoelectric Materials 

Thermoelectric materials prediction 




  1. S. Bauer, P. Benner, T. Bereau, V. Blum, M. Boley, C. Carbogno, C. R. A. Catlow, G. Dehm, S. Eibl, R. Ernstorfer, Á. Fekete, L. Foppa, P. Fratzl, C. Freysoldt, B. Gault, L. M. Ghiringhelli, S. K. Giri, A. Gladyshev, P. Goyal, J. Hattrick-Simpers, L. Kabalan, P. Karpov, M. S. Khorrami, C. Koch, S. Kokott, T. Kosch, I. Kowalec, K. Kremer, A. Leitherer, Y. Li, C. H. Liebscher, A. J. Logsdail, Z. Lu, F. Luong, A. Marek, F. Merz, J. R. Mianroodi, J. Neugebauer, T. A. R. Purcell, D. Raabe, M. Rampp, M. Rossi, J.-M. Rost, U. Saalmann, A. Saxena, L. Sbailo, M. Scheffler, M. Scheidgen, M. Schloz, D. F. Schmidt, S. Teshuva, A. Trunschke, Y. Wei, G. Weikum, R. P. Xian, Y. Yao, M. Zhao,
    Roadmap on Data-Centric Materials Science.
    accepted May 17, 2024 in Modelling Simul. Mater. Sci. Eng; https://doi.org/10.1088/1361-651X/ad4d0d
    Preprint Download (2024): pdf



  1. J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
    Interpretable Machine Learning for Materials Design.
    Journal of Materials Research (2023); https://doi.org/10.1557/s43578-023-01164-w 
    Download: pdf

  2. F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno,
    Anharmonicity in Thermal Insulators – An Analysis from First Principles.
    Phys. Rev. Lett. 130, 236301 (2023); https://doi.org/10.1103/PhysRevLett.130.236301
    Download: pdf

  3. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli, C. Carbogno,
    Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity.
    npj Computational Materials 9 (1), 112 (2023); https://doi.org/10.1038/s41524-023-01063-y
    Download: pdf

  4. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli,
    Recent advances in the SISSO method and their implementation in the SISSO++ code.
    J. Chem. Phys. 159, 114110 (2023); https://doi.org/10.1063/5.0156620
    Download (2023): pdf



  1. L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, and L. M. Ghiringhelli,
    Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites .
    Physical Review Letters 129, 55301 (2022); https://doi.org/10.1103/PhysRevLett.129.055301
    Download: pdf
  2. T. A. R. Purcell, M. Scheffler, C. Carbogno, and L.M. Ghiringhelli,
    SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach.
    Journal of Open Source Software 7 (71), 3960 (2022); https://doi.org/10.21105/joss.03960
    Download: pdf



  1. C. W. Andersen, R. Armiento, E. Blokhin, G. J. Conduit, S. Dwaraknath, M. L. Evans, Á. Fekete, A. Gopakumar, S. Gra┼żulis, A. Merkys, F. Mohamed, C. Oses, G. Pizzi, G.-M. Rignanese, M. Scheidgen, L. Talirz, C. Toher, D. Winston, R. Aversa, K. Choudhary, P. Colinet, S. Curtarolo, D. Di Stefano, C. Draxl, S. Er, M. Esters, M. Fornari, M. Giantomassi, M. Govoni, G. Hautier, V. Hegde, M. K. Horton, P. Huck, G. Huhs, J. Hummelshøj, A. Kariryaa, B. Kozinsky, S. Kumbhar, M. Liu, N. Marzari, A. J. Morris, A. Mostofi, K. A. Persson, G. Petretto, T. A. R. Purcell, F. Ricci, F. Rose, M. Scheffler, D. Speckhard, M. Uhrin, A. Vaitkus, P. Villars, D. Waroquiers, C. Wolverton, M. Wu, and X. Yang,
    OPTIMADE: an API for exchanging materials data.
    Scientific Data 8, 217 (2021); https://doi.org/10.1038/s41597-021-00974-z
    Download: pdf



  1. F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno,
    Anharmonicity Measure for Materials. Phys. Rev. Materials 4, 083809 (2020); https://doi.org/10.1103/PhysRevMaterials.4.083809
    Reprint download: pdf, Arxiv

  2. F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno,
    FHI-vibes: Ab Initio Vibrational Simulations. J. Open Source Softw. 52, 2601 (2020); https://doi.org/10.21105/joss.02671
    Reprint download: pdf



  1. M.-O. Lenz, T. A. R. Purcell, D. Hicks, S. Curtarolo, M. Scheffler, C. Carbogno,
    Parametrically constrained geometry relaxations for high-throughput materials science. npj Computational Materials 5, 123 (2019); https://doi.org/10.1038/s41524-019-0254-4
    Reprint download: pdf

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