Fabrizio, AlbertoMeyer, BenjaminFabregat, RaimonCorminboeuf, Clemence2020-01-152020-01-152020-01-152019-12-0110.2533/chimia.2019.983https://infoscience.epfl.ch/handle/20.500.14299/164605WOS:000504762100003In 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.Chemistry, MultidisciplinaryChemistrycatalysisfree-energy landscapesmachine learningquantum chemistryelectron population analysisdensity-functional theoryevolution electrocatalysishydrogen evolutiondesigncomplexesdiscoveryprofilesmetalstext::journal::journal article::research article