# Quantum machine learning in chemical space

Faber, Felix Andreas. Quantum machine learning in chemical space. 2019, Doctoral Thesis, University of Basel, Faculty of Science.

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Official URL: http://edoc.unibas.ch/diss/DissB_13439

Assessing and benchmarking the performance of existing machine learning models on various classes of compounds and chemical properties is a substantial part of this thesis. These results are used to understand better which machine learning models are best suited for a given combination of properties and compounds. For example, thirteen electronic ground state properties of $\sim$131k organic molecules, calculated at hybrid-DFT level of theory, were used to gauge the predictive accuracy of combinations of representations and regressors. The out-of-sample prediction errors of the models on the hybrid-DFT quality data are on par with, or close to, the CCSD(T) error to experimental values, indicating that reference data need to go beyond hybrid-DFT if QML predictions are to surpass chemical accuracies.
Finally, the applicability of QML models is explored. A machine learning model which encodes the elemental identities of the atoms placed in each site, to exhaustively screen the formation energy of $\sim$2 milion Elpasolite crystals. The resulting model's accuracy improves systematically with additional training data, reaching an accuracy of 0.1 eV/atom when trained on 10k crystals. Out of the $\sim$2 million crystals, we identify 90 unique structures which span the convex hull of stability, among which NFAl$_2$Ca$_6$, with uncommon stoichiometry and a negative atomic oxidation state for Al.