Alexandre Tkatchenko

According to our database, Alexandre Tkatchenko authored at least 15 papers between 2012 and 2025.

Collaborative distances:

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

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

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