Tkatchenko, Alexandre

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data

Tkatchenko, A., & Scheffler, M. (2009) · Physical Review Letters

Advanced Chemical Physics StudiesAdvanced Physical and Chemical Molecular Interactions

Cited by 6,286

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,312

SchNet – A deep learning architecture for molecules and materials

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,217

Reproducibility in density functional theory calculations of solids

Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 1,649

Machine Learning Force Fields

Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,647

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

Tkatchenko, A., DiStasio, R. A., Car, R., & Scheffler, M. (2012) · Physical Review Letters

Advanced Chemical Physics StudiesAdvanced Physical and Chemical Molecular Interactions

Cited by 1,528

Quantum-chemical insights from deep tensor neural networks

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,445

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 849

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 815

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Sandonas, L. M., Tkatchenko, A., Clevert, D.-A., & de Fabritiis, G. (2023) · Chemical Research in Toxicology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 63

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 62

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Sandonas, L. M., Tkatchenko, A., Clevert, D.-A., & de Fabritiis, G. (2023) · Chemical Research in Toxicology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 62