Publications
2022
Articles
- W. Aggoune, A. Eljarrat, D. Nabok, K. Irmscher, M. Zupancic, Z. Galazka, M. Albrecht, C. Koch and C. Draxl,
A consistent picture of excitations in cubic BaSnO3 revealed by combining theory and experiment.
Communications Materials 3, 12 (2022); https://doi.org/10.1088/1361-648X/ac2864
Download: pdf V. Blum, M. Rossi, S. Kokott, and M. Scheffler,
The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale.
Roadmap on electronic structure codes in the exascale era,
Vikram Gavini et al 2023 Modelling Simul. Mater. Sci. Eng. 31 063301; DOI 10.1088/1361-651X/acdf06
Download: pdf- L. Boeri, R.G. Hennig, P.J. Hirschfeld, G. Profeta, A. Sanna, E. Zurek, W.E. Pickett, M. Amsler, R. Dias, M. Eremets, C. Heil, R. Hemley, H. Liu, Y. Ma, C. Pierleoni, A. Kolmogorov, N. Rybin, D. Novoselov, V.I. Anisimov, A.R. Oganov, C.J. Pickard, T. Bi, R. Arita, I. Errea, C. Pellegrini, R. Requist, E.K.U. Gross, E.R. Margine, S.R. Xie, Y. Quan, A. Hire, L. Fanfarillo, G.R. Stewart, J.J. Hamlin, V. Stanev, R.S. Gonnelli, E. Piatti, D. Romanin, D. Daghero and R. Valenti,
The 2021 Room-Temperature Superconductivity Roadmap.
Journal of Physics: Condensed Matter 34 (18), 183002 (2022); https://doi.org/10.1088/1361-648X/ac2864
Download: pdf C. Carbogno, K.S. Thygesen, B. Bieniek, C. Draxl, L.M. Ghiringhelli, A. Gulans, O. T. Hofmann, K. W. Jacobsen, S. Lubeck, J. J. Mortensen, M. Strange, E. Wruss, and M. Scheffler,
Numerical Quality Control for DFT-based Materials Databases.
npj Computational Materials 8, 69 (2022); https://doi.org/10.1038/s41524-022-00744-4
Download: pdfM. Boley and M. Scheffler,
Learning Rules for Materials Properties and Functions.
Roadmap on Machine learning in electronic structure,
Electron. Struct. 4, 023004 (2022); DOI 10.1088/2516-1075/ac572f
Download: pdf- T. Elsaesser, M. Groetschel, M. Scheffler, J. H. Ullrich, F. von Blanckenburg
Open Research Data in Naturwissenschaften und Mathematik.
Empfehlungen der Mathematisch-Naturwissenschaftlichen Klasse der BBAW, ed. by: Der Praesident der Berlin-Brandenburgischen Akademie der Wissenschaften, ISBN:978-3-949455-12-4
https://doi.org/21.11116/0000-000A-CFFA-4
Download: pdf - L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, and L. M. Ghiringhelli,
Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites .
Physical Review Letters 129, 55301 (2022); https://doi.org/10.1103/PhysRevLett.129.055301
Download: pdf - L. Foppa, C. Sutton, L. M. Ghiringhelli, S. De, P. Löser, S.A. Schunk, A. Schäfer, and M. Scheffler,
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence.
ACS Catalysis 12, 2223 (2022); https://doi.org/10.1021/acscatal.1c04793
Download: ACS Publications A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler,
Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides.
Nature Communications 13, 419 (2022); https://doi.org/10.1038/s41467-022-28042-z
Download: pdfE. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler,
Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions.
Journal of Open Source Software, 7 (74), 4040; https://doi.org/10.21105/joss.04040
Download: pdfL. M. Ghiringhelli,
Interpretability of machine-learning models in physical sciences.
Roadmap on Machine learning in electronic structure, ed. by Silvana Botti and Miguel Marques,
H J Kulik et al 2022 Electron. Struct. 4 023004, https://doi.org/10.1088/2516-1075/ac572f
Download: pdfT. A. R. Purcell, M. Scheffler, C. Carbogno, and L.M. Ghiringhelli,
SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach.
Journal of Open Source Software 7 (71), 3960 (2022); https://doi.org/10.21105/joss.03960
Download: pdfB. Regler, M. Scheffler, and L.M. Ghiringhelli,
TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Min Knowl Disc 36, 1815–1864 (2022); https://doi.org/10.1007/s10618-022-00847-y
Download: pdfL. Sbailò, Á. Fekete, L. M. Ghiringhelli, and M. Scheffler
The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding.
npj Computational Materials 8, 250 (2022); https://doi.org/10.1038/s41524-022-00935-z
Download: pdfM. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C. Felser, M. Greiner, A. Groß, C. T. Koch, K. Kremer, W. E. Nagel, M. Scheidgen, C. Wöll, and C. Draxl,
FAIR data enabling new horizons for materials research.
Nature 604, 635 (2022); https://www.doi.org/10.1038/s41586-022-04501-x
Download: pdfC. Tantardini, S. Kokott, X. Gonze, S.V. Levchenko and W.A. Saidi,
“Self-trapping” in solar cell hybrid inorganic-organic perovskite absorbers.
Applied Materials Today 26, 101380 (2022).
Download: pdfA. M. Teale, T. Helgaker, A. Savin, C. Adamo, B. Aradi, A. V. Arbuznikov, P. W. Ayers, E. J. Baerends, V. Barone, P. Calaminici, E. Cancès, E. A. Carter, P. K. Chattaraj, H. Chermette, I. Ciofini, T. D. Crawford, F. De Proft, J. F. Dobson, C. Draxl, T. Frauenheim, E. Fromager, P. Fuentealba, L. Gagliardi, G. Galli, J. Gao, P. Geerlings, N. Gidopoulos, P. M. W. Gill, P. Gori-Giorgi, A. Görling, T. Gould, S. Grimme, O. Gritsenko, H. J. A.Jensen, E. R. Johnson, R. O. Jones, M. Kaupp, A. M. Köster, L. Kronik, A. I. Krylov, S. Kvaal, A. Laestadius, M. Levy, M. Lewin, S. Liu, P.-F. Loos, N. T. Maitra, F. Neese, J. P. Perdew, K. Pernal, P. Pernot, P. Piecuch, E. Rebolini, L. Reining, P. Romaniello, A. Ruzsinszky, D. R. Salahub, M. Scheffler, P. Schwerdtfeger, V. N. Staroverov, J. Sun, E. Tellgren, D. J. Tozer, S. B. Trickey, C. A. Ullrich, A. Vela, G. Vignale, T. A. Wesolowski, X. W. Yang,
DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science.
Phys. Chem. Chem. Phys. 24, 28700-28781 (2022); https://doi.org/10.1039/D2CP02827A
Download: pdfY. Zhou, C. Zhu, M. Scheffler, and L. M. Ghiringhelli,
Ab initio approach for thermodynamic surface phases with full consideration of anharmonic effects – the example of hydrogen at Si(100).
Physical Review Letter 128, 246101 (2022); https:/doi.org/10.1103/PhysRevLett.128.246101
Download: pdf
Ph.D. Thesis
- M.O. Lenz-Himmer,
Towards Efficient Novel Materials Discovery Acceleration of High-throughput Calculations and Semantic Management of Big Data using Ontologies.
HU Berlin, 2022; https://doi.org/10.18452/24340
Download: pdf
Reprint download: pdf - F. Knoop,
Heat transport in strongly anharmonic solids from first principles.
HU Berlin, 2022; https://doi.org/10.18452/24244
Reprint download: pdf - E. Ahmetik,
Artificial Intelligence for Crystal Structure Prediction.
TU Berlin, 2022; https://doi.org/10.14279/depositonce-16033
Reprint download: pdf - Z. Yuan,
Electrical conductivity from first principles.
HU Berlin, 2022.
Reprint download: pdf - B. Regler,
Systematic identification of relevant features for the statistical modeling of materials properties of crystalline solids.
FU Berlin, 2022; http://dx.doi.org/10.17169/refubium-35222
Reprint download: pdf
Master Thesis
- B. Zhao,
Identifying descriptors for the In-silico, high-throughput discovery of the thermal Insulators for thermoelectric applications.
TU Darmstadt, 2022.
Reprint download: pdf - X. Zhu,
Ab Initio green-kubo calculations for strongly anharmonic solids: a comparative benchmark of lattice thermal conductivities.
TU Darmstadt, 2022
Reprint download: pdf
Articles
M. F. Langer, A. Goeßmann, and M. Rupp,
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning.
npj Computational Materials 8, 41 (2022); https://doi.org/10.1038/s41524-022-00721-x
Download: pdf
Ph.D. Thesis
M. Dragoumi,
Quasiparticle energies from second-order perturbation theory.
FU Berlin, 2022;
Download: pdf
Articles
K. S. Belthle, T. Beyazay, C. Ochoa-Hernández, R. Miyazaki, L. Foppa, W. F. Martin, and H. Tüysüz,
Effects of Silica Modification (Mg, Al, Ca, Ti, and Zr) on Supported Cobalt Catalysts for H2-Dependent CO2 Reduction to Metabolic Intermediates.
J. Am. Chem. Soc. 2022, 144, 46, 21232–21243; https://doi.org/10.1021/jacs.2c08845
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