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Prediction of atomization energy using graph kernel and active learning.
J Chem Phys ; 150(4): 044107, 2019 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-30709286
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 effects 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 dataset.





Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Aspecto clínico: Predição / Prognóstico Idioma: Inglês Revista: J Chem Phys Ano de publicação: 2019 Tipo de documento: Artigo País de afiliação: Estados Unidos