FHI
The NOMAD Laboratory

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

Person

Dr. Thomas Purcell

Purcell

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

RESEARCH TOPICS

RESEARCH GROUP: Artificial Intelligence-Assisted Discovery of Thermoelectric Materials 

Thermoelectric materials prediction 

METHODS

2025

Articles

  1. J. Behler, G. Csanyi, L. Foppa, K. Kang, M. F. Langer, J. T. Margraf, Akhil S. Nair, T. A. R. Purcell, P. Rinke, M. Scheffler, 
    Workflows for Artificial Intelligence.
    Submitted for publication in "Roadmap for Advancement of the FHI-aims Software Package", September 5, 2024;
    https://doi.org/10.26434/chemrxiv-2024-vw06p
    Preprint Download (2024): ChemRxiv

  2. K. Kang, T. A. R. Purcell, C. Carbogno, M. Scheffler,
    Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning.
    Submitted for publication September 18, 2024, https://arxiv.org/abs/2409.11808
    Preprint Download (2024): arXiv

2024

Articles

  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.
    Modelling Simul. Mater. Sci. Eng. 32, 063301; https://doi.org/10.1088/1361-651X/ad4d0d
    Download (2024): pdf

2023

Articles

  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

2022

Articles

  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

2021

Articles

  1. C. W. Andersen, R. Armiento, E. Blokhin, G. J. Conduit, S. Dwaraknath, M. L. Evans, Á. Fekete, A. Gopakumar, S. Gražulis, 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

2020

Articles

  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

2019

Articles

  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

Within the publication lists the label [abs,src,ps] links to abstract, source file, and postscript version of the respective paper on the e-print archives of xxx.lanl.gov. Source and postscript files are usually transfered as gz-compressed tar-files. If your browser cannot automatically unpack those files you should proceed as follows:

Save the file as paper.tar.gz Execute the following commands (on a UNIX machine): gunzip paper.tar.gz tar -xvf paper.tar

return

Navigation