TY - JOUR AU - Fabrizio, Alberto AU - Meyer, Benjamin AU - Fabregat, Raimon AU - Corminboeuf, Clemence PY - 2019/12/18 Y2 - 2024/03/28 TI - Quantum Chemistry Meets Machine Learning JF - CHIMIA JA - Chimia VL - 73 IS - 12 SE - Scientific Articles DO - 10.2533/chimia.2019.983 UR - https://www.chimia.ch/chimia/article/view/2019_983 SP - 983 AB - In 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. ER -