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Institution

Fritz Haber Institute of the Max Planck Society

FacilityBerlin, Germany
About: Fritz Haber Institute of the Max Planck Society is a facility organization based out in Berlin, Germany. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 3490 authors who have published 5017 publications receiving 183731 citations. The organization is also known as: Fritz Haber Institute of the Max Planck Society.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors report detailed measurements of the lineshape and intensity of the C-O stretching vibration in the adsorption system Pt{111}-CO using IR reflection-absorption spectroscopy.

270 citations

Journal ArticleDOI
TL;DR: The results show that the approach employed here can reach the demanding accuracy of crystal-structure prediction and organic material design with minimal empiricism.
Abstract: The development and application of computational methods for studying molecular crystals, particularly density-functional theory (DFT), is a large and ever-growing field, driven by their numerous applications. Here we expand on our recent study of the importance of many-body van der Waals interactions in molecular crystals [A. M. Reilly and A. Tkatchenko, J. Phys. Chem. Lett. 4, 1028 (2013)], with a larger database of 23 molecular crystals. Particular attention has been paid to the role of the vibrational contributions that are required to compare experiment sublimation enthalpies with calculated lattice energies, employing both phonon calculations and experimental heat-capacity data to provide harmonic and anharmonic estimates of the vibrational contributions. Exact exchange, which is rarely considered in DFT studies of molecular crystals, is shown to have a significant contribution to lattice energies, systematically improving agreement between theory and experiment. When the vibrational and exact-exchange contributions are coupled with a many-body approach to dispersion, DFT yields a mean absolute error (3.92 kJ/mol) within the coveted "chemical accuracy" target (4.2 kJ/mol). The role of many-body dispersion for structures has also been investigated for a subset of the database, showing good performance compared to X-ray and neutron diffraction crystal structures. The results show that the approach employed here can reach the demanding accuracy of crystal-structure prediction and organic material design with minimal empiricism.

268 citations

Proceedings Article
26 Jun 2017
TL;DR: This work proposes to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid, and obtains a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.
Abstract: Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

267 citations

Journal ArticleDOI
TL;DR: In this paper, the electronic structure of iridium oxide was investigated using synchrotron-based X-ray photoemission and absorption spectroscopies with theoretical calculations.
Abstract: Iridium-based materials are among the most active and stable electrocatalysts for the oxygen evolution reaction. Amorphous iridium oxide structures are found to be more active than their crystalline counterparts. Herein, we combine synchrotron-based X-ray photoemission and absorption spectroscopies with theoretical calculations to investigate the electronic structure of Ir metal, rutile-type IrO2, and an amorphous IrOx. Theory and experiment show that while the Ir 4f line shape of Ir metal is well described by a simple Doniach–Sunjic function, the peculiar line shape of rutile-type IrO2 requires the addition of a shake-up satellite 1 eV above the main line. In the catalytically more active amorphous IrOx, we find that additional intensity appears in the Ir 4f spectrum at higher binding energy when compared with rutile-type IrO2 along with a pre-edge feature in the O K-edge. We identify these additional features as electronic defects in the anionic and cationic frameworks, namely, formally OI− and IrIII, which may explain the increased activity of amorphous IrOx electrocatalysts. We corroborate our findings by in situ X-ray diffraction as well as in situ X-ray photoemission and absorption spectroscopies. Copyright © 2015 John Wiley & Sons, Ltd.

266 citations

Journal ArticleDOI
TL;DR: The theory and philosophy of multivariate statistical classification are reviewed using generalized metrics and problem-dependent classification rationales are proposed and a set of computer-generated “randomly oriented molecular images” are used to test the classification schemes.

264 citations


Authors

Showing all 3514 results

NameH-indexPapersCitations
Jens K. Nørskov184706146151
Qiang Zhang1611137100950
William A. Goddard1511653123322
Matthias Scheffler12575261011
Tao Zhang123277283866
Gerhard Ertl12072057560
James A. Dumesic11861558935
Angel Rubio11093052731
Pavel Hobza10756448080
Hans-Joachim Freund10696246693
Xinhe Bao10382846524
Peter Strasser10035737374
Dang Sheng Su9961536117
Robert Schlögl9270633795
Gianfranco Pacchioni9162232262
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20236
202271
2021242
2020236
2019209
2018173