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pKa-ML-final.pdf (1023.3 kB)
Using Machine Learning to Predict the pKa of C–H Bonds. Relevance to Catalytic Methane Functionalization
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
submitted on 13.07.2020 and posted on 14.07.2020by Christopher Zhou, William Grumbles, Thomas Cundari
Six machine learning models (random forest, neural network, support vector machine, k-nearest neighbors, Bayesian ridge regression, least squares linear regression) were trained on a dataset of 3d transition metal-methyl and -methane complexes to predict pKa(C–H), a property demonstrated to be important in catalytic activity and selectivity. Results illustrate that the machine learning models are quite promising, with RMSE metrics ranging from 4.6 to 8.8 pKa units, despite the relatively modest amount of data available to train on. Importantly, the machine learning models agreed that (a) conjugate base properties were more impactful than those of the corresponding conjugate acid, and (b) the energy of the highest occupied molecular orbital conjugate base was the most significant input feature in the prediction of pKa(C–H). Furthermore, results from additional testing conducted using an external dataset of Sc-methyl complexes demonstrated the robustness of all models, with RMSE metrics ranging from 1.5 to 6.6 pKa units. In all, this research demonstrates the potential of machine learning models in organometallic catalyst development.