In recent years, machine learning (ML) has been extensively attempted on all sections in development of functional materials such as material discovery, determination of experimental factors for the discovered materials, and tuning experimental factors to produce the selected materials. Design of experiment (DoE) and response surface methodology (RSM) are efficient strategies to screen experimental factors and study the correlation between material property and experimental factors. Usually, second order quadratic model is applied to construct the response surface. ML-based models, such as Gaussian process and neural networks, have also be implemented into the RSM. In this work, a ML model based on support vector machine (SVM) was built to analyze the high-throughput experimental data from DoE. Silica xerogel was chosen as a model material, the goal was to control the diversity of the silanol groups on the surfaces of silica. Silica samples were prepared following the central composite design (CCD) in a segmented continuous flow reactor. Sodium silicate and CO2 were used as the raw materials. Both quadratic model and SVM model were studied on the analysis of experimental data. SVM model showed better performance in fitting and predicting, which was attributed to the advantage of handling complex nonlinear correlations. Based on the response surface generated by the SVM model, correlations between experimental factors and diversity of silanol groups were identified. With the guidance of the SVM model, two samples with desired silica surface properties were prepared and attempted as supporting materials for heterogeneous catalysis. The impact of surface silanol groups on the catalytic performance was discussed. (C) 2019 Elsevier Ltd. All rights reserved.