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Opendata, web and dolomites

QML SIGNED

Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy

Total Cost €

EC-Contrib. €

Partnership

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QML project word cloud

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

learning reactants calculations plan time easily data chemists computationally accuracy tool catalysts optimize electronic train machine demanding diverse reactive predictive reaction computational models relaxed chemistry ing synthetic describe property crystals energetic large techniques molecules profiles quantum treatment equilibrium qml

Project "QML" data sheet

The following table provides information about the project.

Coordinator	UNIVERSITAT BASEL Organization address address: PETERSPLATZ 1 city: BASEL postcode: 4051 website: www.unibas.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˙980˙500 €
EC max contribution	1˙980˙500 € (100%)
Programme	1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
Code Call	ERC-2017-COG
Funding Scheme	ERC-COG
Starting year	2018
Duration (year-month-day)	from 2018-06-01 to 2023-05-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	UNIVERSITAT BASEL UNIVERSITAT BASEL Organization address address: PETERSPLATZ 1 city: BASEL postcode: 4051 website: www.unibas.ch contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	CH (BASEL)	coordinator	1˙980˙500.00

Map

Project objective

Large and diverse property data sets of relaxed molecules and crystals, resulting from computationally demanding quantum calculations, have recently been used to train machine learning models of various energetic and electronic properties. We propose to advance these techniques to a level where they can also describe reaction profiles, i.e. reactive non-equilibrium processes which traditionally would require quantum chemistry treatment. The resulting quantum machine learning (QML) models will provide reaction profiles for new reactants in real-time and with quantum accuracy. The overall goal is to develop a predictive computational tool which allows chemists to easily optimize reaction conditions, develop new catalysts, or even plan new synthetic pathways.

Publications

List of publications.
year	authors and title	journal	last update
2019	Stefan Heinen, Max Schwilk, Guido Falk von Rudorff, O. Anatole von Lilienfeld Machine learning the computational cost of quantum chemistry published pages: , ISSN: , DOI:	preprint	2020-02-13
2019	Pal Mezei, O. Anatole von Lilienfeld Non-covalent quantum machine learning corrections to density functionals published pages: , ISSN: , DOI:	preprint	2020-02-13

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