Explore the words cloud of the BeStMo project. It provides you a very rough idea of what is the project "BeStMo" about.
The following table provides information about the project.
Coordinator |
UNIVERSITE DU LUXEMBOURG
Organization address contact info |
Coordinator Country | Luxembourg [LU] |
Project website | https://www.tcpunilu.com/bestmo |
Total cost | 1˙811˙650 € |
EC max contribution | 1˙811˙650 € (100%) |
Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
Code Call | ERC-2016-COG |
Funding Scheme | ERC-COG |
Starting year | 2017 |
Duration (year-month-day) | from 2017-03-01 to 2022-02-28 |
Take a look of project's partnership.
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1 | UNIVERSITE DU LUXEMBOURG | LU (ESCH-SUR-ALZETTE) | coordinator | 1˙811˙650.00 |
We propose focused theory developments and applications, which aim to substantially advance our ability to model and understand the behavior of molecules in complex environments. From a large repertoire of possible environments, we have chosen to concentrate on experimentally-relevant situations, including molecular fluctuations in electric and optical fields, disordered molecular crystals, solvated (bio)molecules, and molecular interactions at/through low-dimensional nanostructures. A challenging aspect of modeling such realistic environments is that both molecular electronic and nuclear fluctuations have to be treated efficiently at a robust quantum-mechanical level of theory for systems with 1000s of atoms. In contrast, the current state of the art in the modeling of complex molecular systems typically consists of Newtonian molecular dynamics employing classical force fields. We will develop radically new approaches for electronic and nuclear fluctuations that unify concepts and merge techniques from quantum-mechanical many-body Hamiltonians, statistical mechanics, density-functional theory, and machine learning. Our developments will be benchmarked using experimental measurements with terahertz (THz) spectroscopy, atomic-force and scanning tunneling microscopy (AFM/STM), time-of-flight (TOF) measurements, and molecular interferometry.
Our final goal is to bridge the accuracy of quantum mechanics with the efficiency of force fields, enabling large-scale predictive quantum molecular dynamics simulations for complex systems containing 1000s of atoms, and leading to novel conceptual insights into quantum-mechanical fluctuations in large molecular systems. The project goes well beyond the presently possible applications and once successful will pave the road towards having a suite of first-principles-based modeling tools for a wide range of realistic materials, such as biomolecules, nanostructures, disordered solids, and organic/inorganic interfaces.
year | authors and title | journal | last update |
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2019 |
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi Machine learning for molecular simulation published pages: , ISSN: , DOI: |
arXiv preprint | 2020-04-04 |
2020 |
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. DFTB+, a software package for efficient approximate density functional theory based atomistic simulations published pages: 124101, ISSN: 0021-9606, DOI: 10.1063/1.5143190 |
The Journal of Chemical Physics 152/12 | 2020-04-04 |
2019 |
Prashanth S. Venkataram, Jan Hermann, Teerit J. Vongkovit, Alexandre Tkatchenko, Alejandro W. Rodriguez Impact of nuclear vibrations on van der Waals and Casimir interactions at zero and finite temperature published pages: eaaw0456, ISSN: 2375-2548, DOI: 10.1126/sciadv.aaw0456 |
Science Advances 5/11 | 2020-04-03 |
2019 |
O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko Exploring Chemical Compound Space with Quantum-Based Machine Learning published pages: , ISSN: , DOI: |
arXiv preprint | 2020-04-03 |
2019 |
Johannes Hoja, Hsin-Yu Ko, Marcus A. Neumann, Roberto Car, Robert A. DiStasio, Alexandre Tkatchenko Reliable and practical computational description of molecular crystal polymorphs published pages: eaau3338, ISSN: 2375-2548, DOI: 10.1126/sciadv.aau3338 |
Science Advances 5/1 | 2019-11-07 |
2018 |
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, K.-R. Müller SchNetPack: A Deep Learning Toolbox For Atomistic Systems published pages: 448-455, ISSN: 1549-9618, DOI: 10.1021/acs.jctc.8b00908 |
Journal of Chemical Theory and Computation 15/1 | 2019-11-07 |
2019 |
Yasmine S. Al-Hamdani, Alexandre Tkatchenko Understanding non-covalent interactions in larger molecular complexes from first principles published pages: 10901, ISSN: 0021-9606, DOI: 10.1063/1.5075487 |
The Journal of Chemical Physics 150/1 | 2019-11-07 |
2018 |
Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller Learning representations of molecules and materials with atomistic neural networks published pages: , ISSN: , DOI: |
2019-11-07 | |
2019 |
Martin Stöhr, Alexandre Tkatchenko Quantum Mechanics of Proteins in Explicit Water: The Role of Plasmon-Like Solute-Solvent Interactions published pages: , ISSN: , DOI: |
arXiv preprint | 2019-11-07 |
2019 |
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights published pages: , ISSN: , DOI: |
2019-11-07 | |
2018 |
Wiktor Pronobis, Alexandre Tkatchenko, Klaus-Robert Müller Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules published pages: 2991-3003, ISSN: 1549-9618, DOI: 10.1021/acs.jctc.8b00110 |
Journal of Chemical Theory and Computation 14/6 | 2019-10-29 |
2019 |
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 i-PI 2.0: A universal force engine for advanced molecular simulations published pages: 214-223, ISSN: 0010-4655, DOI: 10.1016/j.cpc.2018.09.020 |
Computer Physics Communications 236 | 2019-10-29 |
2018 |
Majid Mortazavi, Jan Gerit Brandenburg, Reinhard J. Maurer, Alexandre Tkatchenko Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding published pages: 399-405, ISSN: 1948-7185, DOI: 10.1021/acs.jpclett.7b03234 |
The Journal of Physical Chemistry Letters 9/2 | 2019-10-29 |
2019 |
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces published pages: 114102, ISSN: 0021-9606, DOI: 10.1063/1.5078687 |
The Journal of Chemical Physics 150/11 | 2019-10-29 |
2018 |
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, K.-R. Müller SchNet – A deep learning architecture for molecules and materials published pages: 241722, ISSN: 0021-9606, DOI: 10.1063/1.5019779 |
The Journal of Chemical Physics 148/24 | 2019-10-29 |
2018 |
Dmitry V. Fedorov, Mainak Sadhukhan, Martin Stöhr, Alexandre Tkatchenko Quantum-Mechanical Relation between Atomic Dipole Polarizability and the van der Waals Radius published pages: , ISSN: 0031-9007, DOI: 10.1103/PhysRevLett.121.183401 |
Physical Review Letters 121/18 | 2019-10-29 |
2019 |
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko sGDML: Constructing accurate and data efficient molecular force fields using machine learning published pages: 38-45, ISSN: 0010-4655, DOI: 10.1016/j.cpc.2019.02.007 |
Computer Physics Communications 240 | 2019-10-29 |
2017 |
Wei Liu, Yingda Jiang, Karl-Heinz Dostert, Casey P. O’Brien, Wiebke Riedel, Aditya Savara, Swetlana Schauermann, Alexandre Tkatchenko Catalysis beyond frontier molecular orbitals: Selectivity in partial hydrogenation of multi-unsaturated hydrocarbons on metal catalysts published pages: e1700939, ISSN: 2375-2548, DOI: 10.1126/sciadv.1700939 |
Science Advances 3/7 | 2019-06-12 |
2017 |
Mainak Sadhukhan, Alexandre Tkatchenko Long-Range Repulsion Between Spatially Confined van der Waals Dimers published pages: , ISSN: 0031-9007, DOI: 10.1103/PhysRevLett.118.210402 |
Physical Review Letters 118/21 | 2019-04-18 |
2018 |
Andrii Kleshchonok, Alexandre Tkatchenko Tailoring van der Waals dispersion interactions with external electric charges published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-05407-x |
Nature Communications 9/1 | 2019-04-18 |
2018 |
Guo-Xu Zhang, Anthony M Reilly, Alexandre Tkatchenko, Matthias Scheffler Performance of various density-functional approximations for cohesive properties of 64 bulk solids published pages: 63020, ISSN: 1367-2630, DOI: 10.1088/1367-2630/aac7f0 |
New Journal of Physics 20/6 | 2019-04-18 |
2017 |
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, Klaus-Robert Müller Machine learning of accurate energy-conserving molecular force fields published pages: e1603015, ISSN: 2375-2548, DOI: 10.1126/sciadv.1603015 |
Science Advances 3/5 | 2019-04-18 |
2018 |
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko Towards exact molecular dynamics simulations with machine-learned force fields published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-06169-2 |
Nature Communications 9/1 | 2019-04-18 |
2018 |
Mainak Sadhukhan, Alexandre Tkatchenko Sadhukhan and Tkatchenko Reply: published pages: , ISSN: 0031-9007, DOI: 10.1103/PhysRevLett.120.258902 |
Physical Review Letters 120/25 | 2019-04-18 |
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