Alexandre Tkatchenko
Impact in
- Materials Chemistry top 0.05%
- Machine Learning in Materials Science
- Graphene research and applications
- Physical and Theoretical Chemistry top 0.05%
- Crystallography and molecular interactions
Papers in
-
- Machine Learning in Materials Science 75
- Graphene research and applications 25
-
- Advanced Chemical Physics Studies 85
- Spectroscopy and Quantum Chemical Studies 36
- Quantum, superfluid, helium dynamics 33
- Co-authors
- Matthias Scheffler (23 shared papers)Klaus‐Robert Müller (22 shared papers)O. Anatole von Lilienfeld (11 shared papers)Kristof T. Schütt (9 shared papers)Robert A. DiStasio (14 shared papers)K. Müller (5 shared papers)Stefan Chmiela (15 shared papers)Huziel E. Sauceda (12 shared papers)
- Journals
- Physical Review Letters (28 papers)The Journal of Chemical Physics (23 papers)Journal of Chemical Theory and Computation (22 papers)The Journal of Physical Chemistry Letters (18 papers)Nature Communications (17 papers)
- Partner nations
- LuxembourgGermanyUnited States
In The Last Decade
Alexandre Tkatchenko
236 papers receiving 26.3k citations
Alexandre Tkatchenko's Hit Papers
Peers
Comparison fields: 5 of 175
- Materials Chemistry 17.4k
- Physical and Theoretical Chemistry 3.0k
- Atomic and Molecular Physics, and Optics 8.8k
- Computational Theory and Mathematics 4.4k
- Catalysis 1.2k
Countries citing papers authored by Alexandre Tkatchenko
This map shows the geographic impact of Alexandre Tkatchenko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexandre Tkatchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexandre Tkatchenko more than expected).
Fields of papers citing papers by Alexandre Tkatchenko
This network shows the impact of papers produced by Alexandre Tkatchenko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexandre Tkatchenko. The network helps show where Alexandre Tkatchenko may publish in the future.
Co-authors
The 25 scholars most cited alongside Alexandre Tkatchenko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 242 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data Hit paper breakdown → | 2009 | 5121 |
| 2 | Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Hit paper breakdown → | 2012 | 1606 |
| 3 | SchNet – A deep learning architecture for molecules and materials Hit paper breakdown → | 2018 | 1469 |
| 4 | Accurate and Efficient Method for Many-Body van der Waals Interactions Hit paper breakdown → | 2012 | 1206 |
| 5 | Quantum-chemical insights from deep tensor neural networks Hit paper breakdown → | 2017 | 990 |
| 6 | Machine learning of accurate energy-conserving molecular force fields Hit paper breakdown → | 2017 | 828 |
| 7 | Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Hit paper breakdown → | 2015 | 599 |
| 8 | Resolution-of-identity approach to Hartree–Fock, hybrid density functionals, RPA, MP2 andGWwith numeric atom-centered orbital basis functions Hit paper breakdown → | 2012 | 597 |
| 9 | Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems Hit paper breakdown → | 2021 | 583 |
| 10 | First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications Hit paper breakdown → | 2017 | 478 |
| 11 | Density-Functional Theory with Screened van der Waals Interactions for the Modeling of Hybrid Inorganic-Organic Systems Hit paper breakdown → | 2012 | 478 |
| 12 | Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies Hit paper breakdown → | 2013 | 451 |
| 13 | SchNetPack: A Deep Learning Toolbox For Atomistic Systems Hit paper breakdown → | 2018 | 320 |
| 14 | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Hit paper breakdown → | 2019 | 307 |
| 15 | 2010 | 289 | |
| 16 | 2013 | 284 | |
| 17 | Materials perspective on Casimir and van der Waals interactions Hit paper breakdown → | 2016 | 282 |
| 18 | 2013 | 272 | |
| 19 | 2009 | 268 | |
| 20 | 2011 | 259 |
About Alexandre Tkatchenko
Alexandre Tkatchenko is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Computational Theory and Mathematics and Molecular Biology, having authored 242 papers that have together received 26.6k indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (85 papers), Machine Learning in Materials Science (75 papers), Computational Drug Discovery Methods (36 papers), Spectroscopy and Quantum Chemical Studies (36 papers), Quantum, superfluid, helium dynamics (33 papers), Molecular Junctions and Nanostructures (29 papers), Protein Structure and Dynamics (29 papers) and Graphene research and applications (25 papers). The work is most often cited by research in Materials Chemistry (17.4k citations), Physical and Theoretical Chemistry (3.0k citations), Atomic and Molecular Physics, and Optics (8.8k citations), Computational Theory and Mathematics (4.4k citations) and Catalysis (1.2k citations). Alexandre Tkatchenko has collaborated with scholars based in Luxembourg, Germany and United States. Frequent co-authors include Matthias Scheffler, Klaus‐Robert Müller, O. Anatole von Lilienfeld, Kristof T. Schütt, Robert A. DiStasio, K. Müller, Stefan Chmiela, Huziel E. Sauceda, Matthias Rupp and Anthony M. Reilly. Their work appears in journals such as Physical Review Letters, The Journal of Chemical Physics, Journal of Chemical Theory and Computation, The Journal of Physical Chemistry Letters and Nature Communications.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.