Novel Materials Discovery at the FHI of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin
Learn More About Us
Events
Library @FHI
Infos & Services
Publications
Publications of the NOMAD Laboratory
Use our Publications Search:
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 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
Download: pdf- V. Blum, M. Rossi, S. Kokott, and M. Scheffler,
The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale.
Modelling and Simulation in Materials Science and Engineering 30 (2022).
Preprint Download: arXiv - 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 M. Boley and M. Scheffler,
Learning Rules for Materials Properties and Functions.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Electron. Struct. 4, 023004 (2022); DOI 10.1088/2516-1075/ac572f; https://iopscience.iop.org/article/10.1088/2516-1075/ac572f
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, npj Computational Materials 8, 69 (2022); https://doi.org/10.1038/s41524-022-00744-4
Download: pdf - J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
Interpretable Machine Learning for Materials Design.
Preprint Download: arXiv - 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 - 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.
Submitted to Scientific Data on May 29, 2022.
Preprint Download: arXiv - L. M. Ghiringhelli,
Interpretability of machine-learning models in physical sciences.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Preprint Download: arXiv - F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno,
Anharmonicity in Thermal Insulators – An Analysis from First Principles.
(submitted)
Preprint Download: arXiv - F. Knoop, M. Scheffler, and C. Carbogno,
Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
(submitted)
Preprint Download: arXiv - 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: pdf A. Mazheika, Y. Wang, R. Valero, L.M. Ghiringhelli, F. Viñes, F. Illas, S. V. Levchenko, and M. Scheffler,
Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides. Nat. Commun.13, 419 (2022); https://doi.org/10.1038/s41467-022-28042-z
Reprint 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: pdf- T. 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: pdf - T. 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.
Preprint Download: arXiv B. 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: https://www.nature.com/articles/s41524-022-00935-z.pdf- M. 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
Reprint Download: pdf
Preprint Download: arXiv - C. 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: sciencedirect A. 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
2022
Ph.D. Thesis
- E. Ahmetik,
Artificial Intelligence for Crystal Structure Prediction.
TU Berlin, 2022; https://doi.org/10.14279/depositonce-16033
Reprint download: pdf M. Dragoumi,
Quasiparticle energies from second-order perturbation theory.
FU Berlin, 2022;
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 - 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 - 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 - Z. Yuan,
Electrical conductivity from first principles.
HU Berlin, 2022.
Reprint download: pdf
2022
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
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 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
Download: pdf- V. Blum, M. Rossi, S. Kokott, and M. Scheffler,
The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale.
Modelling and Simulation in Materials Science and Engineering 30 (2022).
Preprint Download: arXiv - 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 M. Boley and M. Scheffler,
Learning Rules for Materials Properties and Functions.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Electron. Struct. 4, 023004 (2022); DOI 10.1088/2516-1075/ac572f; https://iopscience.iop.org/article/10.1088/2516-1075/ac572f
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, npj Computational Materials 8, 69 (2022); https://doi.org/10.1038/s41524-022-00744-4
Download: pdf - J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
Interpretable Machine Learning for Materials Design.
Preprint Download: arXiv - 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 - 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.
Submitted to Scientific Data on May 29, 2022.
Preprint Download: arXiv - L. M. Ghiringhelli,
Interpretability of machine-learning models in physical sciences.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Preprint Download: arXiv - F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno,
Anharmonicity in Thermal Insulators – An Analysis from First Principles.
(submitted)
Preprint Download: arXiv - F. Knoop, M. Scheffler, and C. Carbogno,
Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
(submitted)
Preprint Download: arXiv - 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: pdf A. Mazheika, Y. Wang, R. Valero, L.M. Ghiringhelli, F. Viñes, F. Illas, S. V. Levchenko, and M. Scheffler,
Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides. Nat. Commun.13, 419 (2022); https://doi.org/10.1038/s41467-022-28042-z
Reprint 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: pdf- T. 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: pdf - T. 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.
Preprint Download: arXiv B. 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: https://www.nature.com/articles/s41524-022-00935-z.pdf- M. 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
Reprint Download: pdf
Preprint Download: arXiv - C. 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: sciencedirect A. 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
2022
Ph.D. Thesis
- E. Ahmetik,
Artificial Intelligence for Crystal Structure Prediction.
TU Berlin, 2022; https://doi.org/10.14279/depositonce-16033
Reprint download: pdf M. Dragoumi,
Quasiparticle energies from second-order perturbation theory.
FU Berlin, 2022;
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 - 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 - 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 - Z. Yuan,
Electrical conductivity from first principles.
HU Berlin, 2022.
Reprint download: pdf
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 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
Download: pdf- V. Blum, M. Rossi, S. Kokott, and M. Scheffler,
The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale.
Modelling and Simulation in Materials Science and Engineering 30 (2022).
Preprint Download: arXiv - 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 M. Boley and M. Scheffler,
Learning Rules for Materials Properties and Functions.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Electron. Struct. 4, 023004 (2022); DOI 10.1088/2516-1075/ac572f; https://iopscience.iop.org/article/10.1088/2516-1075/ac572f
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, npj Computational Materials 8, 69 (2022); https://doi.org/10.1038/s41524-022-00744-4
Download: pdf - J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
Interpretable Machine Learning for Materials Design.
Preprint Download: arXiv - 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 - 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.
Submitted to Scientific Data on May 29, 2022.
Preprint Download: arXiv - L. M. Ghiringhelli,
Interpretability of machine-learning models in physical sciences.
Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
Preprint Download: arXiv - F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno,
Anharmonicity in Thermal Insulators – An Analysis from First Principles.
(submitted)
Preprint Download: arXiv - F. Knoop, M. Scheffler, and C. Carbogno,
Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
(submitted)
Preprint Download: arXiv - 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: pdf A. Mazheika, Y. Wang, R. Valero, L.M. Ghiringhelli, F. Viñes, F. Illas, S. V. Levchenko, and M. Scheffler,
Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides. Nat. Commun.13, 419 (2022); https://doi.org/10.1038/s41467-022-28042-z
Reprint 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: pdf- T. 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: pdf - T. 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.
Preprint Download: arXiv B. 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: https://www.nature.com/articles/s41524-022-00935-z.pdf- M. 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
Reprint Download: pdf
Preprint Download: arXiv - C. 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: sciencedirect A. 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
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