Bond Type Restricted Property Weighted Radial Distribution Functions for Accurate Machine Learning Prediction of Atomization Energies.

Understanding the performance of machine learning algorithms is essential for designing more accurate and efficient statistical models. It is not always possible to unravel the reasoning of neural networks. Here, we propose a method for calculating machine learning kernels in closed and analytic form by combining atomic property weighted radial distribution function (AP-RDF) descriptor with a Gaussian kernel. This allowed us to analyze and improve the performance of the Bag-of-Bonds descriptor when the bond type restriction is included in AP-RDF. The improvement is achieved for the prediction of molecular atomization energies (MAE = 1.7 kcal/mol for QM7 data set) and is due to the incorporation of a tensor product into the kernel, which captures the multidimensional representation of the AP-RDF. On the other hand, the numerical version of the AP-RDF is a constant size descriptor, making it more computationally efficient than Bag-of-Bonds. We have also discussed a connection between molecular quantum similarity and machine learning kernels with first-principles kinds of descriptors.