Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments

Publications

Peer reviewed articles (21)

Catalysis beyond frontier molecular orbitals: Selectivity in partial hydrogenation of multi-unsaturated hydrocarbons on metal catalysts

Author(s): Wei Liu, Yingda Jiang, Karl-Heinz Dostert, Casey P. O’Brien, Wiebke Riedel, Aditya Savara, Swetlana Schauermann, Alexandre Tkatchenko
Published in: Science Advances, Issue 3/7, 2017, Page(s) e1700939, ISSN 2375-2548
DOI: 10.1126/sciadv.1700939

Tailoring van der Waals dispersion interactions with external electric charges

Author(s): Andrii Kleshchonok, Alexandre Tkatchenko
Published in: Nature Communications, Issue 9/1, 2018, ISSN 2041-1723
DOI: 10.1038/s41467-018-05407-x

Performance of various density-functional approximations for cohesive properties of 64 bulk solids

Author(s): Guo-Xu Zhang, Anthony M Reilly, Alexandre Tkatchenko, Matthias Scheffler
Published in: New Journal of Physics, Issue 20/6, 2018, Page(s) 063020, ISSN 1367-2630
DOI: 10.1088/1367-2630/aac7f0

Machine learning of accurate energy-conserving molecular force fields

Author(s): Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, Klaus-Robert Müller
Published in: Science Advances, Issue 3/5, 2017, Page(s) e1603015, ISSN 2375-2548
DOI: 10.1126/sciadv.1603015

Long-Range Repulsion Between Spatially Confined van der Waals Dimers

Author(s): Mainak Sadhukhan, Alexandre Tkatchenko
Published in: Physical Review Letters, Issue 118/21, 2017, ISSN 0031-9007
DOI: 10.1103/PhysRevLett.118.210402

Sadhukhan and Tkatchenko Reply:

Author(s): Mainak Sadhukhan, Alexandre Tkatchenko
Published in: Physical Review Letters, Issue 120/25, 2018, ISSN 0031-9007
DOI: 10.1103/PhysRevLett.120.258902

Towards exact molecular dynamics simulations with machine-learned force fields

Author(s): Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko
Published in: Nature Communications, Issue 9/1, 2018, ISSN 2041-1723
DOI: 10.1038/s41467-018-06169-2

SchNet – A deep learning architecture for molecules and materials

Author(s): K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, K.-R. Müller
Published in: The Journal of Chemical Physics, Issue 148/24, 2018, Page(s) 241722, ISSN 0021-9606
DOI: 10.1063/1.5019779

Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding

Author(s): Majid Mortazavi, Jan Gerit Brandenburg, Reinhard J. Maurer, Alexandre Tkatchenko
Published in: The Journal of Physical Chemistry Letters, Issue 9/2, 2018, Page(s) 399-405, ISSN 1948-7185
DOI: 10.1021/acs.jpclett.7b03234

Quantum-Mechanical Relation between Atomic Dipole Polarizability and the van der Waals Radius

Author(s): Dmitry V. Fedorov, Mainak Sadhukhan, Martin Stöhr, Alexandre Tkatchenko
Published in: Physical Review Letters, Issue 121/18, 2018, ISSN 0031-9007
DOI: 10.1103/PhysRevLett.121.183401

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

Author(s): Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Published in: The Journal of Chemical Physics, Issue 150/11, 2019, Page(s) 114102, ISSN 0021-9606
DOI: 10.1063/1.5078687

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

Author(s): Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Published in: Computer Physics Communications, Issue 240, 2019, Page(s) 38-45, ISSN 0010-4655
DOI: 10.1016/j.cpc.2019.02.007

Understanding non-covalent interactions in larger molecular complexes from first principles

Author(s): Yasmine S. Al-Hamdani, Alexandre Tkatchenko
Published in: The Journal of Chemical Physics, Issue 150/1, 2019, Page(s) 010901, ISSN 0021-9606
DOI: 10.1063/1.5075487

Reliable and practical computational description of molecular crystal polymorphs

Author(s): Johannes Hoja, Hsin-Yu Ko, Marcus A. Neumann, Roberto Car, Robert A. DiStasio, Alexandre Tkatchenko
Published in: Science Advances, Issue 5/1, 2019, Page(s) eaau3338, ISSN 2375-2548
DOI: 10.1126/sciadv.aau3338

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Author(s): K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, K.-R. Müller
Published in: Journal of Chemical Theory and Computation, Issue 15/1, 2018, Page(s) 448-455, ISSN 1549-9618
DOI: 10.1021/acs.jctc.8b00908

Quantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions

Author(s): Martin Stöhr, Alexandre Tkatchenko
Published in: Science Advances, Issue 5/12, 2019, Page(s) eaax0024, ISSN 2375-2548
DOI: 10.1126/sciadv.aax0024

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

Author(s): Tristan Bereau, Robert A. DiStasio, Alexandre Tkatchenko, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 148/24, 2018, Page(s) 241706, ISSN 0021-9606
DOI: 10.1063/1.5009502

Electronic Exchange and Correlation in van der Waals Systems: Balancing Semilocal and Nonlocal Energy Contributions

Author(s): Jan Hermann, Alexandre Tkatchenko
Published in: Journal of Chemical Theory and Computation, Issue 14/3, 2018, Page(s) 1361-1369, ISSN 1549-9618
DOI: 10.1021/acs.jctc.7b01172

Impact of nuclear vibrations on van der Waals and Casimir interactions at zero and finite temperature

Author(s): Prashanth S. Venkataram, Jan Hermann, Teerit J. Vongkovit, Alexandre Tkatchenko, Alejandro W. Rodriguez
Published in: Science Advances, Issue 5/11, 2019, Page(s) eaaw0456, ISSN 2375-2548
DOI: 10.1126/sciadv.aaw0456

DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

Author(s): B. Hourahine, B. Aradi, V. Blum, F. Bonafé, A. Buccheri, C. Camacho, C. Cevallos, M. Y. Deshaye, T. Dumitrică, A. Dominguez, S. Ehlert, M. Elstner, T. van der Heide, J. Hermann, S. Irle, J. J. Kranz, C. Köhler, T. Kowalczyk, T. Kubař, I. S. Lee, V. Lutsker, R. J. Maurer, S. K. Min, I. Mitchell, C. Negre, T. A. Niehaus, A. M. N. Niklasson, A. J. Page, A. Pecchia, G. Penazzi, M. P. Persson, J.
Published in: The Journal of Chemical Physics, Issue 152/12, 2020, Page(s) 124101, ISSN 0021-9606
DOI: 10.1063/1.5143190

i-PI 2.0: A universal force engine for advanced molecular simulations

Author(s): Venkat Kapil, Mariana Rossi, Ondrej Marsalek, Riccardo Petraglia, Yair Litman, Thomas Spura, Bingqing Cheng, Alice Cuzzocrea, Robert H. Meißner, David M. Wilkins, Benjamin A. Helfrecht, Przemysław Juda, Sébastien P. Bienvenue, Wei Fang, Jan Kessler, Igor Poltavsky, Steven Vandenbrande, Jelle Wieme, Clemence Corminboeuf, Thomas D. Kühne, David E. Manolopoulos, Thomas E. Markland, Jeremy O. Rich
Published in: Computer Physics Communications, Issue 236, 2019, Page(s) 214-223, ISSN 0010-4655
DOI: 10.1016/j.cpc.2018.09.020

Book chapters (2)

van der Waals Interactions in Material Modelling

Author(s): Jan Hermann, Alexandre Tkatchenko
Published in: Handbook of Materials Modeling - Methods: Theory and Modeling, 2018, Page(s) 1-33
DOI: 10.1007/978-3-319-42913-7_6-1

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Author(s): Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
Published in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Issue 11700, 2019, Page(s) 311-330
DOI: 10.1007/978-3-030-28954-6_17

Other (4)

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Author(s): Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Published in: 2019

Learning representations of molecules and materials with atomistic neural networks

Author(s): Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Published in: 2018

Exploring Chemical Compound Space with Quantum-Based Machine Learning

Author(s): O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
Published in: arXiv preprint, 2019

Machine learning for molecular simulation

Author(s): Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Published in: arXiv preprint, 2019