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ORCIDAlexandre Tkatchenko
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2020 – today
- 2024
[c4]Mathias Hilfiker
, Leonardo Medrano Sandonas, Marco Klähn, Ola Engkvist, Alexandre Tkatchenko:
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, Alexandre Tkatchenko:
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, Alexandre Tkatchenko, Kostiantyn Chernichenko:
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, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller:
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, Klaus-Robert Müller, Alexandre Tkatchenko:
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, O. Anatole von Lilienfeld:
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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