Systematic Convergence of the Numerical Taylor Series to the Best Standard and Its Potential Implication for the Development of Composite Methods

A Preliminary Neural Network-Based Composite Method for Accurate Prediction of Enthalpies of Formation

A composite method, named ANN-G3S, is introduced, adapting from G3S theory and employing distinct sets of multiplicative scale factors. An artificial neural network (ANN)-based classification model is utilized to select optimal sets of four scale factors for electronic correlation and basis set expansion terms in electronic systems. The correlation and basis set terms are scaled by four parameters, two for atoms and the other two for molecules. The ANN model is trained on the G3/05 test set to identify the best parameter set for each electronic system. To validate the method, 10% of the structures from the test set are randomly excluded from training and optimization, forming a separate validation set. The method demonstrates a mean deviation of 1.11 kcal mol-1 for the G3/05 set and 0.89 kcal mol-1 for the validation set, close to the value presented by the G4 method and surpassing the accuracy of the G3 method of 1.19 kcal mol-1 with significantly reduced computational cost. This method shows advantages by eliminating the need for purely empirical corrections, thereby enhancing both efficiency and accuracy in predicting heats of formation.