Use our Publications Search:
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
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
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.
Journal of Open Source Software, 7 (74), 4040; https://doi.org/10.21105/joss.04040
Download: pdf
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: 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
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: 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
M. Dragoumi,
Quasiparticle energies from second-order perturbation theory.
FU Berlin, 2022;
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
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
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.
Journal of Open Source Software, 7 (74), 4040; https://doi.org/10.21105/joss.04040
Download: pdf
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: 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
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: 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
M. Dragoumi,
Quasiparticle energies from second-order perturbation theory.
FU Berlin, 2022;
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
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
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.
Journal of Open Source Software, 7 (74), 4040; https://doi.org/10.21105/joss.04040
Download: pdf
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: 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
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: 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
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