Alexandre Tkatchenko

According to our database, Alexandre Tkatchenko authored at least 22 papers between 2012 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning.

Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Jurásková, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, O. Anatole von Lilienfeld, Milica Todorovic, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti

CoRR, April, 2026

MBD-ML: Many-body dispersion from machine learning for molecules and materials.

Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, Alexandre Tkatchenko

CoRR, February, 2026

A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.

Hugo MacDermott-Opeskin, Jenke Scheen, Cas Wognum, Joshua T. Horton, Devany West, Alexander Matthew Payne, Maria A. Castellanos, Sean Colby, Edward J. Griffen, David Cousins, Jessica Stacey, Lauren Reid, Jasmin Cara Aschenbrenner, Daren Fearon, Blake Balcomb, Peter Marples, Charles W. E. Tomlinson, Ryan Lithgo, Andre S. Godoy, Max Winokan, Haim Barr, Noa Lahav, Michael Lavi, Shirley Duberstein, Galit Cohen, Gwendolyn Fate, Bruce Lefker, Ralph Robinson, Tamas Szommer, Nick Lynch, David D. L. Minh, Van Ngoc Thuy La, Lulu Kang, Kate Huddleston, Ryan Renslow, Mallory R. Tollefson, W. Patrick Walters, Cynthia Xu, Jonny Hsu, Julien St-Laurent, Honore Etsmoberg, Lu Zhu, Andrew Quirke, Mohamed Iliyas Abdul Haleem, Irfan Alibay, Gunjan Baid, Benjamin Birnbaum, Kevin P. Bishop, Hugo-Javier Bohórquez, Ashmita Bose, C. J. Brown, Jackson W. Burns, Lianjin Cai, Ruel Cedeno, Stephane de Cesco, Vladimir I. Chupakhin, Finlay Clark, Daniel J. Cole, Carles Corbi-Verge, Muhammad Danial, Alec Davi, Wim Dehaen, Niklas Piet Doering, Alexis Dougha, Marie-Pierre Dréanic, Bryce Eakin, Anatol Ehrlich, Rokas Elijosius, Jozef Fülöp, Anthony Gitter, Kenneth Goossens, Yaowen Gu, Teresa Head-Gordon, Laurent Hoffer, Johan Hofmans, Ellena Jiang, Benjamin Kaminow, Sina Khosravi, Asma Feriel Khoualdi, Eelke B. Lenselink, Zhirong Liu, Yue Liu, Sijie Liu, Yizhou Ma, Patrick Maher, Imke Mayer, Oscar Mendez-Lucio, Antonia S. J. S. Mey, Julien Michel, Floriane Montanari, Taoyu Niu, Ryusei Ogino, Ashok Palaniappan, Xiaolin Pan, Auro Patnaik, Long-Hung Dinh Pham, Luis Pinto, Justin Purnomo, Alex Rich, Lars L. Schaaf, Christoph Schran, Rajeev Kumar Singh, Mounika Srilakshmi, Satya Pratik Srivastava, Kunyang Sun, Zhaoxi Sun, Valerij Talagayev, Balamurugan Thirukonda Subramanian Balakrishnan, Ida Titus, Alexandre Tkatchenko, Wojtek Treyde, Giovanni Tricarico, Austin Tripp, Nopsinth Vithayapalert, Yingze Wang, Azmine Toushik Wasi, Steffen Wedig, Gerhard Wolber, Bofei Xu, Weijun Zhou, Frank von Delft, Alpha Lee, Karla Kirkegaard, Peter Sjö, James S. Fraser, John D. Chodera

J. Chem. Inf. Model., 2026

Improving the Stability and Transferability of Effective ADMET Models by Adding Quantum Mechanical Descriptors.

Ashmita Bose, Gian-Luca R. Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif, Nils Weskamp, Alexandre Tkatchenko

J. Chem. Inf. Model., 2026

2025

Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.

Alessio Fallani, Ramil I. Nugmanov, Jose A. Arjona-Medina, Jörg Kurt Wegner, Alexandre Tkatchenko, Kostiantyn Chernichenko

J. Cheminformatics, December, 2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions.

Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller, Jiayu Zhou, Hiroko H. Dodge, David Rousseau, Andrey Ustyuzhanin, Ziyun Yan, Mario Lanza, Fabio Sciarrino, Ryo Yoshida, Zhidong Leong, Teck Leong Tan, Qianxiao Li, Adil Kabylda, Igor Poltavsky, Alexandre Tkatchenko, Sherif Abdulkader Tawfik, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Kristin A. Persson, Bryant Y. Li, Vir Karan, Chenru Duan, Haojun Jia, Qiyuan Zhao, Hiroyuki Hayashi, Atsuto Seko, Isao Tanaka, Omar M. Yaghi, Tim Gould, Bun Chan, Stefan Vuckovic, Tianbo Li, Min Lin, Zehcen Tang, Yang Li, Yong Xu, Amrita Joshi, Xiaonan Wang, Leonard W. T. Ng, Sergei V. Kalinin, Mahshid Ahmadi, Jiyizhe Zhang, Shuyuan Zhang, Alexei A. Lapkin, Ming Xiao, Zhe Wu, Kedar Hippalgaonkar, Limsoon Wong, Lorenzo Bastonero, Nicola Marzari, Dorye Luis Esteras Cordoba, Andrei Tomut, Alba Quinones Andrade, Jose-Hugo Garcia

CoRR, November, 2025

Machine learning surrogate models of many-body dispersion interactions in polymer melts.

Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz, Alexandre Tkatchenko, Stéphane P. A. Bordas

CoRR, March, 2025

Atomic orbits in molecules and materials for improving machine learning force fields.

Anton Charkin-Gorbulin, Artem Kokorin, Huziel E. Sauceda, Stefan Chmiela, Claudio Quarti, David Beljonne, Alexandre Tkatchenko, Igor Poltavsky

Mach. Learn. Sci. Technol., 2025

2024

Quantum-informed simulations for mechanics of materials: DFTB+MBD framework.

Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas, Alexandre Tkatchenko, Jakub Lengiewicz

CoRR, 2024

Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules.

Mathias Hilfiker, Leonardo Medrano Sandonas, Marco Klähn, Ola Engkvist, Alexandre Tkatchenko

Proceedings of the AI in Drug Discovery - First International Workshop, 2024

Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer.

Alessio Fallani, José Antonio Arjona-Medina, Konstantin Chernichenko, Ramil I. Nugmanov, Jörg Kurt Wegner, Alexandre Tkatchenko

Proceedings of the AI in Drug Discovery - First International Workshop, 2024

2022

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective.

Alice E. A. Allen, Alexandre Tkatchenko

CoRR, 2022

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations.

Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller

CoRR, 2022

2021

BIGDML: Towards Exact Machine Learning Force Fields for Materials.

Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko

CoRR, 2021

2019

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks.

Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

Proceedings of the Explainable AI: Interpreting, 2019

sGDML: Constructing accurate and data efficient molecular force fields using machine learning.

Stefan Chmiela, Huziel Enoc Sauceda Felix, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko

Comput. Phys. Commun., 2019

Machine learning for molecular simulation.

Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

CoRR, 2019

2018

Learning representations of molecules and materials with atomistic neural networks.

Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller

CoRR, 2018

Quantum-chemical insights from interpretable atomistic neural networks.

Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller

CoRR, 2018

2017

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.

Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2012

Modeling of molecular atomization energies using machine learning.

Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld

J. Cheminformatics, 2012

Learning Invariant Representations of Molecules for Atomization Energy Prediction.

Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012