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Novel Materials Discovery at the FHI of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin
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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.
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
Preprint Download: arXiv - 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: pdf- E. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler,
Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions.
Submitted to Journal of Open Source Software (JOSS) (2022); https://doi.org/10.21105/joss.04040
Preprint Download: arXiv - 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. Submitted to Data Mining and Knowledge Discovery (Jan 30, 2020)
https://doi.org/10.1007/s10618-022-00847-y
Download: pdf L. 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.
Physical Chemistry Chemical Physics (2022), in print. https://doi.org/10.1039/D2CP02827A
Download: pdf Y. 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 - 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
Preprint Download: arXiv - 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: pdf- E. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler,
Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions.
Submitted to Journal of Open Source Software (JOSS) (2022); https://doi.org/10.21105/joss.04040
Preprint Download: arXiv - 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. Submitted to Data Mining and Knowledge Discovery (Jan 30, 2020)
https://doi.org/10.1007/s10618-022-00847-y
Download: pdf L. 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.
Physical Chemistry Chemical Physics (2022), in print. https://doi.org/10.1039/D2CP02827A
Download: pdf Y. 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
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