Prediction and synthesis of novel layered double hydroxide with desired basal spacing based on relevance vector machine

Abstract A Quantitative Structure Property Relationship (QSPR) model for the basal spacing of layered double hydroxide is developed in the present work by using generic algorithms feature selection, and relevance vector machine regression. The relative error of the developed model is 0.78% in cross-validation. Then, the QSPR model is applied to recommend a LDH with desired basal spacing for synthesis. The synthesized LDH meets the design requirement, thereby confirming the prediction power of the developed model.