Machine learning-assisted design of Al2O3–SiO2 porous ceramics based on few-shot datasets

Abstract

A machine learning model was proposed to accelerate the preparation of Al2O3–SiO2 porous ceramics (ASPC) based on few-shot datasets. Phosphate tailings and bauxite were used as raw materials to prepare ASPC and obtain the initial dataset. The accuracy and generalization ability of the model are heavily influenced by the dataset partitioning method. 10-fold cross-validation and random sampling were compared to identify the optimal approach for training and testing the model on the limited data. Four commonly used regression algorithms of RF, KNN, SVR and BPNN were selected to predict the mechanical properties of ASPC. Based on the evaluation of model performance, the random forest algorithm was found to be the most suitable for small sample datasets. Using the trained random forest model, the feature importance of ASPC performance was analyzed, and a new ASPC database was established. Experimental schemes with the desired performance were obtained through screening from ASPC database. Finally, the ASPC with the target performance was prepared experimentally. The result demonstrated that employing machine learning techniques on few-shot datasets could effectively tackle the issues of low efficiency and insufficient data samples.