FHI
The NOMAD Laboratory

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

Publications

Publications of the NOMAD Laboratory

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2024

Articles

  1. G. Bellini, F. Girgsdies, G. Koch, S. J. Carey, O. Timpe, G. Auffermann, M. Scheffler, R. Schlögl, L. Foppa, A. Trunschke,
    CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and AI.
    Submitted for publication April 15, 2024, https://doi.org/10.26434/chemrxiv-2024-8xkh5
    Preprint Download (2024): ChemRxiv 

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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.
    J. Chem. Phys. 161, 024112 (2024), https://doi.org/10.1063/5.0208103 
    Download (2024): pdf

  7. 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

  8. R. Miyazaki, S. Faraji, S. Levchenko, L. Foppa, M. Scheffler,
    Assessment of mBEEF and RPBE Exchange-Correlation Functionals for Describing the Adsorption of C2H2 and C2H4 on Transition-Metal Surfaces.
    Submitted for publication May 24, 2024, https://doi.org/10.26434/chemrxiv-2024-k9mq6-v2 
    Preprint Download (2024): ChemRxiv

  9. 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

  10. 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

  11. 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

 

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