Publications
Publications of the NOMAD Laboratory
Use our Publications Search:
2023
Articles
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
Preprint Download (2023): arXivJ. 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)
Download: pdfL. 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: pdfL. 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. 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: pdfH. 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)
Download: pdfF. 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)
Download: pdfF. 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)
Download: pdfM. 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: pdfM. 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: pdfA. 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: pdfM. 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.
Submitted to JOSS (March 24, 2023)R. Miyazaki, K. S. Belthle, H. Tüysüz, L. Foppa, M. Scheffler,
Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: an AI Approach Integrating Theoretical and Experimental Data.
ChemRxiv. Cambridge: Cambridge Open Engage; 2023; https://doi.org/10.26434/chemrxiv-2023-xx4f1
Preprint Download (2023): pdfO. 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)
Download: pdfS. 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
Preprint Download (2023): arXivT. 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)
Download: pdfT. 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)
Download (2023): pdf
Within the publication lists the label [abs,src,ps] links to abstract, source file, and postscript version of the respective paper on the e-print archives of xxx.lanl.gov. Source and postscript files are usually transfered as gz-compressed tar-files. If your browser cannot automatically unpack those files you should proceed as follows:
Save the file as paper.tar.gz Execute the following commands (on a UNIX machine): gunzip paper.tar.gz tar -xvf paper.tar