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Publications of the NOMAD Laboratory

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2024

Articles

1. G. Bellini, G. Koch, F. Girgsdies, J. Dong, 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 Artificial Intelligence.
   Angew. Chem. Int. Ed. 2024 DOI: 10.1002/anie.202417812 
   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.
   In Roadmap on Data-Centric Materials Science, Section 2.1.
   Modelling Simul. Mater. Sci. Eng. 32, 063301; https://doi.org/10.1088/1361-651X/ad4d0d 
   Download (2024): pdf

4. L. Foppa, M. Scheffler,
   Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance.
   In Roadmap on Data-Centric Materials Science, Section 2.3. 
   Modelling Simul. Mater. Sci. Eng. 32, 063301; https://doi.org/10.1088/1361-651X/ad4d0d   
   Download (2024): pdf

5. J. Quan, C. Carbogno, M. Scheffler,
   Carrier Mobility of Strongly Anharmonic Materials from First Principles.
   Phys. Rev. B 110, 235202 (2024), https://doi.org/10.1103/PhysRevB.110.235202
   Download (2024): pdf

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,
   Vibrational frequencies utilized for the assessment of exchange-correlation functionals in the description of metal-adsorbate systems: C₂H₂ and C₂H₄ on transition-metal surfaces.
   Catal. Sci. Technol., 2024,14, 6924-6933; https://doi.org/10.1039/D4CY00685B 
   Download (2024): pdf

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

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

11. A. Trunschke, L. Foppa, M. Scheffler,
    Clean-Data Concept for Experimental Studies.
    In Roadmap on Data-Centric Materials Science, Section 3.2. 
    Modelling Simul. Mater. Sci. Eng. 32, 063301; https://doi.org/10.1088/1361-651X/ad4d0d    
    Download (2024): pdf