FHI
The NOMAD Laboratory

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

Publications

Publications of the NOMAD Laboratory

Use our Publications Search:

2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 Before1990


 

 

 

 

 

2023

Articles

  1. 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): arXiv

  2. J. 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: pdf

  3. L. 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: pdf

  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. 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: pdf

  6. H. 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: pdf

  7. F. 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: pdf

  8. F. 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: pdf

  9. M. 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: pdf

  10. M. 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: pdf

  11. A. 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: pdf

  12. M. 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)

  13. 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): pdf

  14. O. 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: pdf 

  15. 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
    Preprint Download (2023): arXiv

  16. T. 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: pdf

  17. T. 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