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Decoding, Mapping and Designing the Structural Complexity of Hydrogen-Bond Networks: from Water to Proteins to Polymers

Total Cost €


EC-Contrib. €






 HBMAP project word cloud

Explore the words cloud of the HBMAP project. It provides you a very rough idea of what is the project "HBMAP" about.

organic    linear    necessarily    recurring    sufficiently    accessible    confinement    comprehend    accurately    behavior    proteins    treat    interpret    water    derives    materials    sampling    probabilistic    patterns    underpinning    simulation    rationalizing    compounds    kevlar    motifs    allosteric    quantum    wealth    bonded    recognition    first    efficient    principles    atomistic    investigations    strategy    biomimetic    structure    amorphous    ubiquitous    data    machine    solutes    manipulate    flexibility    networks    ambient    interfaces    propensity    transitions    rests    mapping    microscopic    coarse    polymers    contexts    bond    variables    computational    techniques    learning    deep    dimensionality    acceleration    crystalline    diagram    liquid    fundamental    topological    govern    complexity    energetically    hold    nature    form    reversible    describe    labile    efforts    physical    dynamical    inorganic    biological    electrons    hydrogen    protons    structural    landscape    extensive    collective    simulations    grained    despite    translate    configuration    subject    astrophysical    bonds    experiments    bonding    compound    intense   

Project "HBMAP" data sheet

The following table provides information about the project.


Organization address
address: BATIMENT CE 3316 STATION 1
postcode: 1015

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 Switzerland [CH]
 Project website
 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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


year authors and title journal last update
List of publications.
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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