The NOMAD Laboratory

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


Publications of the NOMAD Laboratory

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  1. S. Bi, C. Carbogno, I. Y. Zhang, M. Scheffler,
    Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework.
    J. Chem. Phys. 160, 034106 (2024), https://doi.org/10.1063/5.0178075
    Download: pdf

  2. M. Boley, F. Luong, S. Teshuva, D. F. Schmidt, L. Foppa, M. Scheffler,
    From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery.
    submitted November 27, 2023; https://doi.org/10.48550/arXiv.2311.15549
    Preprint Download (2023): arXiv

  3. L. Foppa, M. Scheffler,
    Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance.
    submitted for publication November 17, 2023, https://doi.org/10.48550/arXiv.2311.10381
    Preprint Download (2023): arXiv

  4. L. Foppa, M. Scheffler,
    Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups.
    Submitted for publication March 28, 2024, http://arxiv.org/abs/2403.18437
    Preprint Download (2024): arXiv

  5. S. Kokott, F. Merz, Y. Yao, C. Carbogno, M. Rossi, V. Havu, M. Rampp, M. Scheffler, V. Blum,
    Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms.
    submitted March 15, 2024; https://arxiv.org/abs/2403.10343v1
    Preprint Download (2024): arXiv

  6. R. Miyazaki, K. S. Belthle, H. Tüysüz, L. Foppa, M. Scheffler,
    Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data.
    J. Am. Chem. Soc. 2024, 146, 8, 5433–5444; https://doi.org/10.1021/jacs.3c12984
    Download (2024): pdf

  7. 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.
    submitted to Modelling Simul. Mater. Sci. Eng., January 15, 2024; https://doi.org/10.48550/arXiv.2402.10932
    Preprint Download (2024): arXiv

  8. M. Scheffler,
    AI guided workflows for efficiently screening the materials space.
    Coshare Science 02, video-2, 1-18 (2024); https://doi.org/10.61109/cs.202403.129
    Watch video now: video

  9. S. Lu, L. M. Ghiringhelli, C. Carbogno, J. Wang, M. Scheffler,
    On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials.
    submitted for publication September 1, 2023; https://doi.org/10.48550/arXiv.2309.00195
    Preprint Download (2023): arXiv