Project description DEENESFRITPL Transport phenomena through the combined lens of classical and quantum descriptions Attempting to merge classical descriptions of our world with quantum ones often proves quite challenging. This is true when it comes to transport coefficients that govern the movement of conserved quantities such as mass and energy. Many have been defined via a combination of molecular dynamics simulations and so-called first principles methods relying on established laws of nature. However, when light nuclei are involved rather than classical point-like nuclei, quantum effects can emerge, with significant impact on transport phenomena and corresponding coefficients. The EU-funded TRANQUIL project is exploiting molecular dynamics techniques that include nuclear quantum effects in the transport properties of complex ionic liquids and developing a platform to support their massively parallel simulations. Show the project objective Hide the project objective Objective Transport coefficients govern the irreversible flow of extensive, conserved quantities, like mass, momentum, charge and energy. They are fundamental in both science and technology, governing from battery- and fuel-cell-efficiency to the lifecycle of planets. During the last decades, transport coefficients have been successfully extracted from equilibrium molecular dynamics (MD) simulations, according to the Green-Kubo theory of linear response. The theory has been also recently reformulated in ab-initio framework, thanks to the widespread use of density functional theory and new theoretical advancements, like the so-called gauge-invariance principle or novel data-analysis techniques. Despite these great advancements, when light nuclei are present, nuclear quantum effects can arise -like quantum tunnelling and zero-point energy effects-, which are not considered with standard molecular dynamics simulations with classical, point-like nuclei, and may strongly affect transport coefficients. In TRANQUIL we shall employ imaginary-time path integral molecular dynamics techniques to include NQEs in the transport properties of complex ionic liquids, relevant in energy-management technology and planetary science. Machine learning models will be exploited to construct ab-initio accurate force fields for faster MD simulations, as well as to define the atomic properties that are necessary to obtain well-defined microscopic fluxes needed in GK theory, like the dynamical charge or the local energy of each atom. TRANQUIL will also develop new, highly-scalable, and open-source software platform to manage the massively parallel MD simulations required, through the deployment of a targeted secondment. Within TRANQUIL, the experienced researcher will extend his scientific network of collaborations, and learn new leadership skills to boost his career as EU scientist in Condensed Matter theory and reach a full scientific independence. Fields of science natural sciencesmathematicsapplied mathematicsdynamical systemsnatural sciencescomputer and information sciencescomputational sciencemultiphysicsnatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE Net EU contribution € 203 149,44 Address Batiment ce 3316 station 1 1015 Lausanne Switzerland See on map Region Schweiz/Suisse/Svizzera Région lémanique Vaud Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
