2021
DOI: 10.26434/chemrxiv.14635896.v1
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Machine Learning of Quasiparticle Energies in Molecules and Clusters

Abstract: <div> <div> <div> <p>We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both re… Show more

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