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

5 papers align trajectories

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.

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 →
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 →
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 →
Accurate and Efficient Method for Many-Body van der Waals Interactions

2012 1.2k citations Alexandre Tkatchenko et al. Physical Review Letters profile →
Quantum-chemical insights from deep tensor neural networks

2017 967 citations Stefan Chmiela, K. Müller et al. profile →
Machine learning of accurate energy-conserving molecular force fields

2017 810 citations Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances profile →
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. The Journal of Physical Chemistry Letters profile →
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 →
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

2021 546 citations John A. Keith, Valentín Vassilev-Galindo et al. profile →
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 →
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 →
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 →
SchNetPack: A Deep Learning Toolbox For Atomistic Systems

2018 305 citations Michael Gastegger, Alexandre Tkatchenko et al. Journal of Chemical Theory and Computation profile →
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

2019 303 citations Michael Gastegger, Alexandre Tkatchenko et al. profile →
Materials perspective on Casimir and van der Waals interactions

2016 273 citations Alexandre Tkatchenko et al. profile →
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 →
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 →

Peers

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

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20 of 20 papers shown

#	Work	Indexed citations
1	Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach 2025 ·Chemical Physics Letters ·(unknown), (unknown), Stefan Chmiela, Alexandre Tkatchenko, Leeor Kronik	1
2	Chalcogen Bonding with Telluronium Cations: toward Selective Population of Tellurium σ-Holes by Lewis Bases 2025 ·The Journal of Organic Chemistry ·(unknown), Yann Cornaton, Patrick Pale, (unknown), Alexandre Tkatchenko, Jean‐Pierre Djukic, Victor Mamane	1
3	Analyzing Atomic Interactions in Molecules as Learned by Neural Networks 2025 ·Journal of Chemical Theory and Computation ·(unknown), (unknown), Jonas Lederer, Adil Kabylda, Grégoire Montavon, Alexandre Tkatchenko, K. Müller	6
4	Noncovalent Interactions in Density Functional Theory: All the Charge Density We Do Not See 2025 ·Journal of the American Chemical Society ·(unknown), Matteo Gori, Carolin Müller, Alexandre Tkatchenko	0
5	Plasticization of a stiff pharmaceutical solid for better tabletability via cocrystallization: Shape synthons as supramolecular protecting groups 2024 ·Process Safety and Environmental Protection ·Amit Mondal, Biswajit Bhattacharya, (unknown), (unknown), Susobhan Das, Surojit Bhunia, Somnath Dey, Rituparno Chowdhury, Manjima Bhattacharya, Alexandre Tkatchenko, Lewis L. Stevens, Changquan Calvin Sun, C. Malla Reddy	0
6	TIHI Toolkit: A Peak Finder and Analyzer for Spectroscopic Data 2024 ·ACS Omega ·(unknown), Ariadni Boziki, Alexandre Tkatchenko, Joshua T. Berryman	1
7	Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol 2024 ·Journal of Chemical Theory and Computation ·Apurba Nandi, Priyanka Pandey, Paul L. Houston, Chen Qu, Qi Yu, Riccardo Conte, Alexandre Tkatchenko, Joel M. Bowman	9
8	Quantum Drude oscillators coupled with Coulomb potential as an efficient model for bonded and non-covalent interactions in atomic dimers 2024 ·The Journal of Chemical Physics ·(unknown), (unknown), Alexandre Tkatchenko	2
9	Modeling molecular ensembles with gradient-domain machine learning force fields 2023 ·Digital Discovery ·(unknown), Igor Poltavsky, Valentín Vassilev-Galindo, Alexandre Tkatchenko, John A. Keith	13
10	libMBD: A general-purpose package for scalable quantum many-body dispersion calculations 2023 ·The Journal of Chemical Physics ·Jan Hermann, Martin Stöhr, (unknown), (unknown), Bálint Aradi, Reinhard J. Maurer, Alexandre Tkatchenko	14
11	Molecules in Environments: Toward Systematic Quantum Embedding of Electrons and Drude Oscillators 2023 ·Physical Review Letters ·(unknown), (unknown), Leonardo Medrano Sandonas, Alexandre Tkatchenko	10
12	Accurate global machine learning force fields for molecules with hundreds of atoms breakdown → 2023 ·Science Advances ·Stefan Chmiela, Valentín Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus‐Robert Müller	112
13	Correlated Wave Functions for Electron–Positron Interactions in Atoms and Molecules 2022 ·Journal of Chemical Theory and Computation ·Jorge Charry, (unknown), Alexandre Tkatchenko	14
14	Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems 2020 ·arXiv (Cornell University) ·Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani, (unknown), Michele Ceriotti, John D. Chodera, Aaron R. Dinner, Andrew L. Ferguson, Jean‐Bernard Maillet, Hervé Minoux, Christine Peter, Fabio Pietrucci, (unknown), Alexandre Tkatchenko, (unknown), Rafal Wiewiora, Tony Lelièvre	138
15	Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields 2020 ·The Journal of Chemical Physics ·Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, Alexandre Tkatchenko	34
16	Nonlocal Electronic Correlations in the Cohesive Properties of High-Pressure Hydrogen Solids 2020 ·The Journal of Physical Chemistry Letters ·Tingting Cui, Jian‐Chen Li, Wang Gao, Jan Hermann, Alexandre Tkatchenko, Qing Jiang	8
17	Coulomb Interactions between Dipolar Quantum Fluctuations in van der Waals Bound Molecules and Materials 2020 ·arXiv (Cornell University) ·Martin Stöhr, (unknown), Yasmine S. Al-Hamdani, Jan Hermann, Alexandre Tkatchenko	30
18	Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces 2019 ·Bulletin of the American Physical Society ·Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus‐Robert Müller, Alexandre Tkatchenko	1
19	Properties of the water to boron nitride interaction: From zero to two dimensions with benchmark accuracy 2017 ·The Journal of Chemical Physics ·Yasmine S. Al-Hamdani, Mariana Rossi, Dario Alfè, (unknown), (unknown), Jan Gerit Brandenburg, Andrea Zen, Georg Kresse, Andreas Grüneis, Alexandre Tkatchenko, Angelos Michaelides	43
20	Preface 1987 ·Annual Review of Physical Chemistry ·Frank Noé, Alexandre Tkatchenko, Klaus‐Robert Müller, Cecilia Clementi, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), 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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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