Raimon Fabregat

TL;DR: A general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates.

TL;DR: This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high abinitio level to demonstrate the relevance of accessing the ab initio free energy landscapes of molecules.

TL;DR: In this article, an intramolecular singlet fission (iSF) has shown potential to improve the power conversion efficiency in photovoltaic devices by promoting the splitting of a photon-absorbing singlet exciton into two...

TL;DR: In this account, it is demonstrated 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 the authors are tackling.

TL;DR: It is revealed that it is indeed possible to combine such improved photoswitching characteristics while preserving the regular thermal stability of phenyl‐azoheteroarenes, and increased t1/2 values under the appropriate connectivity and bridge length.