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We report an evaluation of a semi-empirical quantum chemical method PM7 fromthe perspective of uncertainty quantification. Specifically, we applyBound-to-Bound Data Collaboration, an uncertainty quantification framework, tocharacterize a) variability of PM7 model parameter values consistent with theuncertainty in the training data, and b) uncertainty propagation from thetraining data to the model predictions. Experimental heats of formation of ahomologous series of linear alkanes are used as the property of interest. Thetraining data are chemically accurate, i.e., they have very low uncertainty bythe standards of computational chemistry. The analysis does not find evidenceof PM7 consistency with the entire data set considered as no single set ofparameter values is found that captures the experimental uncertainties of alltraining data. Nevertheless, PM7 is found to be consistent for subsets of thetraining data. In such cases, uncertainty propagation from the chemicallyaccurate training data to the predicted values preserves error within bounds ofchemical accuracy if predictions are made for the molecules of comparable size.Otherwise, the error grows linearly with the relative size of the molecules.

Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method