A feature restoration for machine learning on anti-corrosion materials

Materials informatics often struggles with small datasets. Our study introduces the Gaussian Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature correlation by generating virtual samples. Applied to six small and one large dataset of 218 N-heterocyclic compounds, GMM-VSG significantly improved predictive performance. Random Forest’s R 2 rose from 0.80 to 0.99, with RMSE dropping from 9.87 to 0.22. Kernel Ridge’s R 2 increased from 0.70 to 0.99, and RMSE decreased from 10.08 to 0.83. KNN improved from R 2 of 0.74–0.90. ANN and MLPNN also saw notable improvements. GMM-VSG is thus crucial for advancing anti-corrosion material research.