Alexandre Tkatchenko

39.7k total citations · 17 hit papers
241 papers, 25.8k citations indexed

About

Alexandre Tkatchenko is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, Alexandre Tkatchenko has authored 241 papers receiving a total of 25.8k indexed citations (citations by other indexed papers that have themselves been cited), including 141 papers in Materials Chemistry, 137 papers in Atomic and Molecular Physics, and Optics and 42 papers in Electrical and Electronic Engineering. Recurrent topics in Alexandre Tkatchenko's work include Advanced Chemical Physics Studies (84 papers), Machine Learning in Materials Science (75 papers) and Computational Drug Discovery Methods (36 papers). Alexandre Tkatchenko is often cited by papers focused on Advanced Chemical Physics Studies (84 papers), Machine Learning in Materials Science (75 papers) and Computational Drug Discovery Methods (36 papers). Alexandre Tkatchenko collaborates with scholars based in Luxembourg, Germany and United States. Alexandre Tkatchenko's co-authors include Matthias Scheffler, Klaus‐Robert Müller, O. Anatole von Lilienfeld, Robert A. DiStasio, Kristof T. Schütt, K. Müller, Stefan Chmiela, Anthony M. Reilly, Matthias Rupp and Huziel E. Sauceda and has published in prestigious journals such as Science, Chemical Reviews and Proceedings of the National Academy of Sciences.

In The Last Decade

Alexandre Tkatchenko

231 papers receiving 25.4k citations

Hit Papers

Accurate Molecular Van De... 2009 2026 2014 2020 2009 2012 2018 2012 2017 1000 2.0k 3.0k 4.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
    2009 5.0k citations Alexandre Tkatchenko et al. Physical Review Letters profile →
  2. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
    2012 1.6k citations Alexandre Tkatchenko, Klaus‐Robert Müller et al. Physical Review Letters profile →
  3. SchNet – A deep learning architecture for molecules and materials
    2018 1.4k citations Huziel E. Sauceda, Alexandre Tkatchenko et al. The Journal of Chemical Physics profile →
  4. Accurate and Efficient Method for Many-Body van der Waals Interactions
    2012 1.2k citations Alexandre Tkatchenko et al. Physical Review Letters profile →
  5. Quantum-chemical insights from deep tensor neural networks
    2017 967 citations Stefan Chmiela, K. Müller et al. profile →
  6. Machine learning of accurate energy-conserving molecular force fields
    2017 810 citations Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances profile →
  7. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
    2015 585 citations Klaus‐Robert Müller, Alexandre Tkatchenko et al. profile →
  8. Resolution-of-identity approach to Hartree–Fock, hybrid density functionals, RPA, MP2 andGWwith numeric atom-centered orbital basis functions
    2012 584 citations Alexandre Tkatchenko et al. profile →
  9. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
    2021 546 citations John A. Keith, Valentín Vassilev-Galindo et al. profile →
  10. Density-Functional Theory with Screened van der Waals Interactions for the Modeling of Hybrid Inorganic-Organic Systems
    2012 474 citations Alexandre Tkatchenko et al. Physical Review Letters profile →
  11. First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications
    2017 462 citations Jan Hermann, Alexandre Tkatchenko et al. profile →
  12. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
    2013 443 citations Grégoire Montavon, Alexandre Tkatchenko et al. Journal of Chemical Theory and Computation profile →
  13. SchNetPack: A Deep Learning Toolbox For Atomistic Systems
    2018 305 citations Alexandre Tkatchenko, K. Müller et al. Journal of Chemical Theory and Computation profile →
  14. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
    2019 303 citations Alexandre Tkatchenko, K. Müller et al. profile →
  15. Materials perspective on Casimir and van der Waals interactions
    2016 273 citations Alexandre Tkatchenko et al. profile →
  16. Accurate global machine learning force fields for molecules with hundreds of atoms
    2023 112 citations Stefan Chmiela, Valentín Vassilev-Galindo et al. Science Advances profile →
  17. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
    2024 67 citations Oliver T. Unke, Martin Stöhr et al. Science Advances profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Alexandre Tkatchenko 17.0k 8.7k 5.3k 4.3k 3.0k 241 25.8k
Mark E. Tuckerman 8.5k 0.5× 12.4k 1.4× 4.6k 0.9× 877 0.2× 5.4k 1.8× 225 29.5k
Stefan Goedecker 10.2k 0.6× 7.2k 0.8× 4.3k 0.8× 869 0.2× 919 0.3× 161 18.7k
Gábor Cśanyi 14.8k 0.9× 3.2k 0.4× 3.2k 0.6× 3.7k 0.9× 2.1k 0.7× 175 18.3k
Roberto Car 24.2k 1.4× 17.4k 2.0× 11.3k 2.1× 916 0.2× 2.3k 0.8× 338 44.6k
Marcus Elstner 6.8k 0.4× 7.6k 0.9× 4.0k 0.7× 847 0.2× 7.6k 2.5× 229 21.5k
Bartosz A. Grzybowski 13.2k 0.8× 2.3k 0.3× 5.1k 1.0× 1.8k 0.4× 5.3k 1.7× 363 30.1k
Lars Goerigk 12.1k 0.7× 7.0k 0.8× 5.5k 1.0× 738 0.2× 2.3k 0.8× 76 29.8k
Thomas Frauenheim 24.3k 1.4× 11.9k 1.4× 12.0k 2.3× 516 0.1× 3.0k 1.0× 716 38.2k
Jörg Behler 13.1k 0.8× 4.2k 0.5× 2.6k 0.5× 3.5k 0.8× 2.2k 0.7× 121 15.7k
Glenn Martyna 6.8k 0.4× 7.1k 0.8× 2.4k 0.5× 840 0.2× 5.9k 1.9× 143 20.9k

Countries citing papers authored by Alexandre Tkatchenko

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Alexandre Tkatchenko

This figure shows the co-authorship network connecting the top 25 collaborators of Alexandre Tkatchenko. A scholar is included among the top collaborators of Alexandre Tkatchenko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexandre Tkatchenko. Alexandre Tkatchenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chmiela, Stefan, et al.. (2025). Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach. Chemical Physics Letters. 867. 141964–141964. 1 indexed citations
2.
Cornaton, Yann, et al.. (2025). Chalcogen Bonding with Telluronium Cations: toward Selective Population of Tellurium σ-Holes by Lewis Bases. The Journal of Organic Chemistry. 90(24). 8254–8268. 1 indexed citations
3.
Lederer, Jonas, et al.. (2025). Analyzing Atomic Interactions in Molecules as Learned by Neural Networks. Journal of Chemical Theory and Computation. 21(2). 714–729. 6 indexed citations
4.
Кокорин, А. И., Huziel E. Sauceda, Stefan Chmiela, et al.. (2025). Atomic orbits in molecules and materials for improving machine learning force fields. Machine Learning Science and Technology. 6(3). 35005–35005. 1 indexed citations
5.
Gori, Matteo, et al.. (2025). Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See. Journal of the American Chemical Society. 147(44). 40763–40775.
6.
Mondal, Amit, Biswajit Bhattacharya, Susobhan Das, et al.. (2024). Plasticization of a stiff pharmaceutical solid for better tabletability via cocrystallization: Shape synthons as supramolecular protecting groups. Process Safety and Environmental Protection. 210. 506–512.
7.
Nandi, Apurba, Priyanka Pandey, Paul L. Houston, et al.. (2024). Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol. Journal of Chemical Theory and Computation. 20(20). 8807–8819. 9 indexed citations
8.
Tkatchenko, Alexandre, et al.. (2024). Quantum Drude oscillators coupled with Coulomb potential as an efficient model for bonded and non-covalent interactions in atomic dimers. The Journal of Chemical Physics. 160(9). 2 indexed citations
9.
Müller, Carolin, et al.. (2024). S pai NN: equivariant message passing for excited-state nonadiabatic molecular dynamics. Chemical Science. 15(38). 15880–15890. 9 indexed citations
10.
Boziki, Ariadni, et al.. (2024). TIHI Toolkit: A Peak Finder and Analyzer for Spectroscopic Data. ACS Omega. 9(50). 49397–49410. 1 indexed citations
11.
Poltavsky, Igor, et al.. (2023). Modeling molecular ensembles with gradient-domain machine learning force fields. Digital Discovery. 2(3). 871–880. 13 indexed citations
12.
Chmiela, Stefan, Valentín Vassilev-Galindo, Oliver T. Unke, et al.. (2023). Accurate global machine learning force fields for molecules with hundreds of atoms. Science Advances. 9(2). eadf0873–eadf0873. 112 indexed citations breakdown →
13.
Hermann, Jan, et al.. (2023). libMBD: A general-purpose package for scalable quantum many-body dispersion calculations. The Journal of Chemical Physics. 159(17). 14 indexed citations
14.
Sandonas, Leonardo Medrano, et al.. (2023). Molecules in Environments: Toward Systematic Quantum Embedding of Electrons and Drude Oscillators. Physical Review Letters. 131(22). 228001–228001. 10 indexed citations
15.
Gkeka, Paraskevi, Gabriel Stoltz, Amir Barati Farimani, et al.. (2020). Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems. arXiv (Cornell University). 138 indexed citations
16.
Stöhr, Martin, Leonardo Medrano Sandonas, & Alexandre Tkatchenko. (2020). Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks. Open Repository and Bibliography (University of Luxembourg). 68 indexed citations
17.
Stöhr, Martin, et al.. (2020). Coulomb Interactions between Dipolar Quantum Fluctuations in van der Waals Bound Molecules and Materials. arXiv (Cornell University). 30 indexed citations
18.
Al-Hamdani, Yasmine S. & Alexandre Tkatchenko. (2019). Understanding non-covalent interactions in larger molecular complexes from first principles. The Journal of Chemical Physics. 150(1). 10901–10901. 89 indexed citations
19.
Al-Hamdani, Yasmine S., Mariana Rossi, Dario Alfè, et al.. (2017). Properties of the water to boron nitride interaction: From zero to two dimensions with benchmark accuracy. The Journal of Chemical Physics. 147(4). 44710–44710. 43 indexed citations
20.
Noé, Frank, et al.. (1987). Preface. Annual Review of Physical Chemistry. 38(1).

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.

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