Publications
Publications of the NOMAD Laboratory
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2024
Articles
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: pdfM. 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): arXivL. 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): arXivL. 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): arXivS. 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): arXivR. 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): pdfS. 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): arXivM. 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: videoS. 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