ERC Consolidator Grant · 2017
Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy
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…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.