Objetivo 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. Ámbito científico natural sciencescomputer and information sciencesdata sciencenatural sciencesbiological sciencesbiochemistrybiomoleculesproteinsnatural scienceschemical sciencespolymer sciencesnatural sciencesphysical sciencesmolecular and chemical physics Programa(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Tema(s) ERC-StG-2015 - ERC Starting Grant Convocatoria de propuestas ERC-2015-STG Consulte otros proyectos de esta convocatoria Régimen de financiación ERC-STG - Starting Grant Institución de acogida ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE Aportación neta de la UEn € 1 500 000,00 Dirección BATIMENT CE 3316 STATION 1 1015 Lausanne Suiza Ver en el mapa Región Schweiz/Suisse/Svizzera Région lémanique Vaud Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 1 500 000,00 Beneficiarios (1) Ordenar alfabéticamente Ordenar por aportación neta de la UE Ampliar todo Contraer todo ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE Suiza Aportación neta de la UEn € 1 500 000,00 Dirección BATIMENT CE 3316 STATION 1 1015 Lausanne Ver en el mapa Región Schweiz/Suisse/Svizzera Région lémanique Vaud Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 1 500 000,00