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Machine learning for NMR spectroscopy

Prediction of the spin system of small molecules from high-resolution liquid NMR spectra with the use of machine learning

Description

The goal of this project is to make NMR spectroscopy available to a wider range of applications and to non-experts by the automation of data reduction and analysis steps, in particular by combining deep learning methods for the extraction and a Bayesian approach for the integration and refinement of information.

Key Data

Projectlead

Dr. Andreas Henrici

Deputy Projectlead

Prof. Dr. Dirk Wilhelm

Project team

Dr. Simon Bruderer, Dr. Flavio De Lorenzi, Dr. Michael Fey, Giulia Fischetti, Prof. Dr. Rudolf Marcel Füchslin, Dominik Graf, Dr. Björn Heitmann, Benjamin Heuberger, Dr. Leila Mohammadzadeh, Dr. Federico Paruzzo, Nicolas Schmid, Dr. Giuseppe Toscano, Dr. Simone Ulzega, Dr. Thomas Oskar Weinmann

Project partners

Bruker Switzerland AG

Project status

completed, 11/2020 - 07/2023

Institute/Centre

Institute of Applied Mathematics and Physics (IAMP); Institute of Computational Life Sciences (ICLS); School of Engineering

Funding partner

Innovationsprojekt / Projekt Nr. 44786.1 IP-ENG

Project budget

571'499 CHF

Publications

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