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PushQChem SIGNED

Pushing Quantum Chemistry by Advancing Photoswitchable Catalysis

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EC-Contrib. €

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Partnership

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Project "PushQChem" data sheet

The following table provides information about the project.

Coordinator
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 

Organization address
address: BATIMENT CE 3316 STATION 1
city: LAUSANNE
postcode: 1015
website: www.epfl.ch

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]
 Total cost 1˙949˙385 €
 EC max contribution 1˙949˙385 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-COG
 Funding Scheme ERC-COG
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2024-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE CH (LAUSANNE) coordinator 1˙949˙385.00

Map

 Project objective

This project exploits the synergy between the trending area of artificial molecular machines and cutting edge computational chemistry approaches. Specific emphasis is placed on photoswitchable catalysts, which respond to external stimuli with a conformational or configurational change. These controllable motions allow catalytic function to be turned ON/OFF in a switch type fashion by opening/hindering access of a substrate to a catalytic site. On one hand, the rich morphology and chemistry of these smart catalysts calls for computational insights and design principles that complement experiment and push the field forward. On the other hand, the inherent complexity of these highly fluxional molecules makes them perfect subjects for driving modern quantum chemistry out of its comfort zone. To benefit from this synergy, the latest innovations in quantum chemistry-based machine learning techniques will be combined with methods capable of thoroughly mapping the intricate chemistry of molecular actuators. Overall, we aim to bridge the gap between the current state-of-the-art, which has reached reasonable quantum chemical accuracy for rigid medium size organic molecules, and more challenging fluxional architectures. The proposed methodological toolbox will be applied to the field of smart catalysis where general strategies for improving the efficiencies and enhancing enantioselectivity will be formulated. Thus, this project involves exploiting a wide range of modern computational approaches to chemical tasks that are broadly relevant to flexible/switchable catalytic systems. The anticipated output will furnish the computational chemistry community with a comprehensive array of novel next-generation approaches with applicability beyond the field of molecular machines.

 Deliverables

List of deliverables.
Data Management Plan Open Research Data Pilot 2020-04-09 20:11:36

Take a look to the deliverables list in detail:  detailed list of PushQChem deliverables.

 Publications

year authors and title journal last update
List of publications.
2020 Raimon Fabregat, Alberto Fabrizio, Benjamin Meyer, Daniel Hollas, Clémence Corminboeuf
Hamiltonian-reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry
published pages: , ISSN: 1549-9618, DOI: 10.1021/acs.jctc.0c00100
Journal of Chemical Theory and Computation 2020-04-04

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

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