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We introduce an explorative active learning (AL) algorithm based on Gaussianprocess regression and marginalized graph kernel (GPR-MGK) to explore chemicalspace with minimum cost. Using high-throughput molecular dynamics simulation togenerate data and graph neural network (GNN) to predict, we constructed anactive learning molecular simulation framework for thermodynamic propertyprediction. In specific, targeting 251,728 alkane molecules consisting of 4 to19 carbon atoms and their liquid physical properties: densities, heatcapacities, and vaporization enthalpies, we use the AL algorithm to select themost informative molecules to represent the chemical space. Validation ofcomputational and experimental test sets shows that only 313 (0.124\% of thetotal) molecules were sufficient to train an accurate GNN model with $\rm R^2 >0.99$ for computational test sets and $\rm R^2 > 0.94$ for experimental testsets. We highlight two advantages of the presented AL algorithm: compatibilitywith high-throughput data generation and reliable uncertainty quantification.

Efficient Chemical Space Exploration Using Active Learning Based on  Marginalized Graph Kernel: an Application for Predicting the Thermodynamic  Properties of Alkanes with Molecular Simulation