FHI
The NOMAD Laboratory

Novel Materials Discovery at the FHI of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin

Publications

Publications of the NOMAD Laboratory

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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. L. Foppa, F. Rüther, M. Geske, G. Koch, F. Girgsdies, P. Kube, S. J. Carey, M. Hävecker, O. Timpe, A. V. Tarasov, M. Scheffler, F. Rosowski, R. Schlögl, and A. Trunschke,
    Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation.
    J. Am. Chem. Soc. 2023, 145, 6, 3427–3442; https://doi.org/10.1021/jacs.2c11117
    Download: pdf

  3. L. M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. T. Koch, M. Kühbach, A. N. Ladines, P. Lambrix, M.-O. Lenz-Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G.-M. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, and M. Scheffler,
    Shared Metadata for Data-Centric Materials Science.
    Scientific Data 10, 626 (2023); https://doi.org/10.1038/s41597-023-02501-8
    Download: pdf

  4. H. Lu, G. Koknat, Y. Yao, J. Hao, X. Qin, C. Xiao, R. Song, F. Merz, M. Rampp, S. Kokott, C. Carbogno, T. Li, G. Teeter, M. Scheffler, J. J. Berry, D. B. Mitzi, J. L. Blackburn, V. Blum, and M. C. Beard,
    Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn.
    PRX Energy 2, 023010 (2023); http://dx.doi.org/10.1103/PRXEnergy.2.023010  
    Download: pdf

  5. 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

  6. F. Knoop, M. Scheffler, and C. Carbogno,
    Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
    Phys. Rev. B 107, 224304 (2023); https://doi.org/10.1103/PhysRevB.107.224304
    Download: pdf

  7. M. F. Langer, F. Knoop. C. Carbogno, M. Scheffler, and M. Rupp, 
    Heat flux for semi-local machine-learning potentials.
    Phys. Rev. B (Letter) 108, L100302 (2023); https://doi.org/10.1103/PhysRevB.108.L100302
    Download: pdf

  8. M. F. Langer, J. T. Frank, F. Knoop,
    Stress and heat flux via automatic differentiation.
    J. Chem. Phys. 159, 174105 (2023); https://doi.org/10.1063/5.0155760 
    Download: pdf

  9. A. Leitherer, B. C. Yeo, C. H. Liebscher, and L. M. Ghiringhelli,
    Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy.
    npj Computational Materials 9, 17 (2023); https://doi.org/10.1038/s41524-023-01133-1
    Download: pdf

  10. M. Scheidgen, L. Himanen, A. N. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. A. Márquez, S. Brockhauser, S. Brückner, L. M. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, P. Ondračka, H.-J. Bungartz, and C. Draxl,
    NOMAD: A distributed web-based platform for managing materials science research data.
    Journal of Open Source Software, 8 (90), 5388; https://doi.org/10.21105/joss.05388
    Download: pdf

  11. O. T. Beynon, A. Owens, C. Carbogno, and A. J. Logsdail,
    Evaluating the Role of Anharmonic Vibrations in Zeolite β Materials.
    J. Phys. Chem. C 127, 16030 (2023); https://doi.org/10.1021/acs.jpcc.3c02863
    Download: pdf 

  12. 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

  13. 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

2023

Ph.D. Thesis

  1. M. F. Langer, 
    Machine Learning for Atomistic Modeling: Representations and Thermal Transport.
    TU Berlin, 2023; https://doi.org/10.14279/depositonce-18647
    Download: pdf

  2. S. Bi, 
    Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework.
    HU Berlin, 2023; https://doi.org/10.18452/26094
    Download: pdf

2023

Master Thesis

  1. F. Fiebig,
    Assessing Electronic Transport in Solid Materials via the Fluctuation-Dissipation Theorem.
    TU Berlin, 2023;
    Download: pdf