Opendata, web and dolomites

BeStMo SIGNED

Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "BeStMo" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITE DU LUXEMBOURG 

Organization address
address: 2 AVENUE DE L'UNIVERSITE
city: ESCH-SUR-ALZETTE
postcode: 4365
website: http://wwwen.uni.lu

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 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

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE DU LUXEMBOURG LU (ESCH-SUR-ALZETTE) coordinator 1˙811˙650.00

Map

 Project objective

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.

 Publications

year authors and title journal last update
List of publications.
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

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "BESTMO" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "BESTMO" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

TransTempoFold (2019)

A need for speed: mechanisms to coordinate protein synthesis and folding in metazoans

Read More  

FatVirtualBiopsy (2020)

MRI toolkit for in vivo fat virtual biopsy

Read More  

PGEN (2019)

Automated evaluation and correction of generation bias in immune receptor repertoires

Read More