Quantum Chemistry Meets Machine Learning
Keywords:Catalysis, Free-energy landscapes, Machine learning, Quantum chemistry
AbstractIn this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.
How to Cite
A. Fabrizio, B. Meyer, R. Fabregat, C. Corminboeuf, Chimia 2019, 73, 983, DOI: 10.2533/chimia.2019.983.
Copyright (c) 2019 Swiss Chemical Society
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.