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Alexandre Tkatchenko
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2020 – today
- 2026
- [j7]Ashmita Bose
, Gian-Luca R. Anselmetti, Matthias Degroote
Improving the Stability and Transferability of Effective ADMET Models by Adding Quantum Mechanical Descriptors. J. Chem. Inf. Model. 66(5): 2488-2500 (2026) - [j6]Hugo MacDermott-Opeskin, Jenke Scheen
A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data. J. Chem. Inf. Model. 66(6): 3129-3149 (2026) - [j5]Tobias Henkes
aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials. J. Chem. Inf. Model. 66(8): 4365-4381 (2026) - [i13]Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, Alexandre Tkatchenko:
MBD-ML: Many-body dispersion from machine learning for molecules and materials. CoRR abs/2602.22086 (2026) - [i12]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:
Perspective: Towards sustainable exploration of chemical spaces with machine learning. CoRR abs/2604.00069 (2026) - [i11]Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro, Silvia Gómez-Coca, Eliseo Ruiz, Alexandre Tkatchenko, Javier Ruiz Hidalgo:
Machine Learning Multiscale Interactions. CoRR abs/2605.25710 (2026) - 2025
- [j4]Alessio Fallani, Ramil I. Nugmanov, Jose A. Arjona-Medina, Jörg Kurt Wegner
Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling. J. Cheminformatics 17(1): 25 (2025) - [j3]Anton Charkin-Gorbulin, Artem Kokorin
Atomic orbits in molecules and materials for improving machine learning force fields. Mach. Learn. Sci. Technol. 6(2): 35005 (2025) - [i10]Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz, Alexandre Tkatchenko, Stéphane P. A. Bordas:
Machine learning surrogate models of many-body dispersion interactions in polymer melts. CoRR abs/2503.15149 (2025) - [i9]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
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions. CoRR abs/2511.20976 (2025) - 2024
- [c4]Mathias Hilfiker
Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules. AIDD@ICANN 2024: 47-57 - [c3]Alessio Fallani, José Antonio Arjona-Medina, Konstantin Chernichenko, Ramil I. Nugmanov, Jörg Kurt Wegner
Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer. AIDD@ICANN 2024: 71-81 - [i8]Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas, Alexandre Tkatchenko, Jakub Lengiewicz:
Quantum-informed simulations for mechanics of materials: DFTB+MBD framework. CoRR abs/2404.04216 (2024) - [i7]Alessio Fallani, Ramil I. Nugmanov, Jose A. Arjona-Medina, Jörg Kurt Wegner
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling. CoRR abs/2410.08024 (2024) - 2022
- [i6]Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations. CoRR abs/2205.08306 (2022) - [i5]Alice E. A. Allen, Alexandre Tkatchenko:
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective. CoRR abs/2211.11680 (2022) - 2021
- [i4]Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko:
BIGDML: Towards Exact Machine Learning Force Fields for Materials. CoRR abs/2106.04229 (2021)
2010 – 2019
- 2019
- [j2]Stefan Chmiela, Huziel Enoc Sauceda Felix, Igor Poltavsky
sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Comput. Phys. Commun. 240: 38-45 (2019) - [p1]Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller:
Quantum-Chemical Insights from Interpretable Atomistic Neural Networks. Explainable AI 2019: 311-330 - [i3]Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi:
Machine learning for molecular simulation. CoRR abs/1911.02792 (2019) - 2018
- [i2]Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller:
Quantum-chemical insights from interpretable atomistic neural networks. CoRR abs/1806.10349 (2018) - [i1]Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller:
Learning representations of molecules and materials with atomistic neural networks. CoRR abs/1812.04690 (2018) - 2017
- [c2]Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller:
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS 2017: 991-1001 - 2012
- [j1]Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller
Modeling of molecular atomization energies using machine learning. J. Cheminformatics 4(S-1): 33 (2012) - [c1]Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller:
Learning Invariant Representations of Molecules for Atomization Energy Prediction. NIPS 2012: 449-457
Coauthor Index
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