Prediction of Atomization Energy Using Graph Kernel and Active Learning

TL;DR

This paper introduces a graph kernel and active learning framework for accurately predicting molecular atomization energies, effectively handling data complexity and confidence estimation with high precision.

Contribution

It presents a novel kernel-based pipeline using Gaussian process regression and active learning for molecular property prediction, emphasizing efficiency and accuracy.

Findings

Achieved a mean absolute error of 0.62 kcal/mol with 2000 training samples

Demonstrated the suitability of the graph kernel for extensive property prediction

Analyzed hyperparameters' effects on accuracy and confidence

Abstract

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effect of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 +- 0.01 kcal/mol using as few as 2000 training samples on the QM7 data set.