HBMAP SIGNED
Decoding, Mapping and Designing the Structural Complexity of Hydrogen-Bond Networks: from Water to Proteins to Polymers
Total Cost €
0EC-Contrib. €
0Partnership
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Project "HBMAP" data sheet
The following table provides information about the project.
| Coordinator |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address contact info |
| Coordinator Country | Switzerland [CH] |
| Project website | https://cosmo.epfl.ch/research/hbmap/ |
| Total cost | 1˙500˙000 € |
| EC max contribution | 1˙500˙000 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2015-STG |
| Funding Scheme | ERC-STG |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-05-01 to 2021-04-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE | CH (LAUSANNE) | coordinator | 1˙500˙000.00 |
Map
Project objective
Hydrogen bonds are ubiquitous and fundamental in nature, underpinning the behavior of systems as different as water, proteins and polymers. Much of this flexibility derives from their propensity to form complex topological networks, which can be strong enough to hold Kevlar together, or sufficiently labile to enable reversible structural transitions in allosteric proteins. Simulations must treat the quantum nature of both electrons and protons to describe accurately the microscopic structure of H-bonded materials, but this wealth of data does not necessarily translate into deep physical understanding. Even the structure of a compound as essential as water is still the subject of intense debate, despite extensive investigations. Identifying recurring bonding patterns is essential to comprehend and manipulate the structural and dynamical properties of H-bonded systems. Our objective is to develop and apply machine-learning techniques to atomistic simulations, and identify the design principles that govern the structure and properties of H-bonded compounds. Our strategy rests on three efforts: (1) recognition of recurring structural motifs with probabilistic data analysis; (2) coarse-grained mapping of the energetically accessible structural landscape by non-linear dimensionality reduction techniques; (3) acceleration of configuration sampling using these data-driven collective variables. Identifying motifs and order parameters will be crucial to interpret simulations and experiments of growing complexity, and will enable computational design of H-bond networks. We will focus first on two objectives. (1) Rationalizing the structure of crystalline, amorphous and liquid water across its phase diagram, from ambient to astrophysical conditions, and its response to solutes, interfaces or confinement. (2) Enabling efficient simulation and structural design of polymers and proteins in non-biological contexts, targeting biomimetic materials and organic/inorganic interfaces.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Andrea Grisafi, Michele Ceriotti Incorporating long-range physics in atomic-scale machine learning published pages: 204105, ISSN: 0021-9606, DOI: 10.1063/1.5128375 |
The Journal of Chemical Physics 151/20 | 2019-12-16 |
| 2019 |
Michael J. Willatt, Félix Musil, Michele Ceriotti Atom-density representations for machine learning published pages: 154110, ISSN: 0021-9606, DOI: 10.1063/1.5090481 |
The Journal of Chemical Physics 150/15 | 2019-11-26 |
| 2019 |
Venkat Kapil, Edgar Engel, Mariana Rossi, Michele Ceriotti Assessment of Approximate Methods for Anharmonic Free Energies published pages: 5845-5857, ISSN: 1549-9618, DOI: 10.1021/acs.jctc.9b00596 |
Journal of Chemical Theory and Computation 15/11 | 2019-11-26 |
| 2019 |
Benjamin A. Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach, Michele Ceriotti A new kind of atlas of zeolite building blocks published pages: 154112, ISSN: 0021-9606, DOI: 10.1063/1.5119751 |
The Journal of Chemical Physics 151/15 | 2019-11-26 |
| 2018 |
Piero Gasparotto, Robert Horst Meißner, Michele Ceriotti Recognizing Local and Global Structural Motifs at the Atomic Scale published pages: , ISSN: 1549-9618, DOI: 10.1021/acs.jctc.7b00993 |
Journal of Chemical Theory and Computation | 2019-07-08 |
| 2019 |
Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti Ab initio thermodynamics of liquid and solid water published pages: 1110-1115, ISSN: 0027-8424, DOI: 10.1073/pnas.1815117116 |
Proceedings of the National Academy of Sciences 116/4 | 2019-09-04 |
| 2018 |
Félix Musil, Michael J. Willatt, Mikhail A. Langovoy, Michele Ceriotti Fast and Accurate Uncertainty Estimation in Chemical Machine Learning published pages: 906-915, ISSN: 1549-9618, DOI: 10.1021/acs.jctc.8b00959 |
Journal of Chemical Theory and Computation 15/2 | 2019-09-04 |
| 2019 |
David M. Wilkins, Andrea Grisafi, Yang Yang, Ka Un Lao, Robert A. DiStasio, Michele Ceriotti Accurate molecular polarizabilities with coupled cluster theory and machine learning published pages: 3401-3406, ISSN: 0027-8424, DOI: 10.1073/pnas.1816132116 |
Proceedings of the National Academy of Sciences 116/9 | 2019-09-04 |
| 2019 |
Michele Ceriotti Unsupervised machine learning in atomistic simulations, between predictions and understanding published pages: 150901, ISSN: 0021-9606, DOI: 10.1063/1.5091842 |
The Journal of Chemical Physics 150/15 | 2019-09-04 |
| 2019 |
Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti Transferable Machine-Learning Model of the Electron Density published pages: 57-64, ISSN: 2374-7943, DOI: 10.1021/acscentsci.8b00551 |
ACS Central Science 5/1 | 2019-09-04 |
| 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 | 2019-09-04 |
| 2018 |
Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley Chemical shifts in molecular solids by machine learning published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-06972-x |
Nature Communications 9/1 | 2019-05-04 |
| 2018 |
Michael J. Willatt, Félix Musil, Michele Ceriotti Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements published pages: 29661-29668, ISSN: 1463-9076, DOI: 10.1039/C8CP05921G |
Physical Chemistry Chemical Physics 20/47 | 2019-05-04 |
| 2018 |
Bingqing Cheng, Christoph Dellago, Michele Ceriotti Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics published pages: 28732-28740, ISSN: 1463-9076, DOI: 10.1039/C8CP04561E |
Physical Chemistry Chemical Physics 20/45 | 2019-05-04 |
| 2018 |
Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials published pages: 241730, ISSN: 0021-9606, DOI: 10.1063/1.5024611 |
The Journal of Chemical Physics 148/24 | 2019-04-18 |
| 2018 |
Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W. Götz, Francesco Paesani Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions published pages: 241725, ISSN: 0021-9606, DOI: 10.1063/1.5024577 |
The Journal of Chemical Physics 148/24 | 2019-04-18 |
| 2018 |
Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs Mapping uncharted territory in ice from zeolite networks to ice structures published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-04618-6 |
Nature Communications 9/1 | 2019-04-18 |
| 2018 |
Andrea Anelli, Edgar A. Engel, Chris J. Pickard, Michele Ceriotti Generalized convex hull construction for materials discovery published pages: , ISSN: 2475-9953, DOI: 10.1103/PhysRevMaterials.2.103804 |
Physical Review Materials 2/10 | 2019-04-18 |
| 2018 |
Thomas E. Markland, Michele Ceriotti Nuclear quantum effects enter the mainstream published pages: 109, ISSN: 2397-3358, DOI: 10.1038/s41570-017-0109 |
Nature Reviews Chemistry 2/3 | 2019-04-18 |
| 2018 |
Mahdi Hijazi, David M. Wilkins, Michele Ceriotti Fast-forward Langevin dynamics with momentum flips published pages: 184109, ISSN: 0021-9606, DOI: 10.1063/1.5029833 |
The Journal of Chemical Physics 148/18 | 2019-04-18 |
| 2018 |
Yair Litman, Davide Donadio, Michele Ceriotti, Mariana Rossi Decisive role of nuclear quantum effects on surface mediated water dissociation at finite temperature published pages: 102320, ISSN: 0021-9606, DOI: 10.1063/1.5002537 |
The Journal of Chemical Physics 148/10 | 2019-04-18 |
| 2018 |
Andrea Grisafi, David M. Wilkins, Gábor Csányi, Michele Ceriotti Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems published pages: , ISSN: 0031-9007, DOI: 10.1103/PhysRevLett.120.036002 |
Physical Review Letters 120/3 | 2019-04-18 |
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