A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites

In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship (QSPR) together. After optimization of the hyper-parameters, certain similarity-based error measures for each query compound were obtained. Clubbing some of these error-based measures with the previously selected features along with the Read-Across prediction function, a number of machine learning models were developed using Partial Least Squares (PLS), Ridge Regression (RR), Linear Support Vector Regression (LSVR), Random Forest (RF) regression, Gradient Boost (GBoost), Adaptive Boosting (Adaboost), Multiple Layer Perceptron (MLP) regression and k-Nearest Neighbor (kNN) regression. Based on the repeated cross-validation as well as external prediction quality and interpretability, the PLS model (nTraining = 38, nTest = 12, R2(Train)= 0.737, Q2 LOO 1/4 0:637; R2 Test 1/4 0:898; Q2 F1oTestTHORN 1/4 0:901THORN was selected as the best predictor which underscored the previously reported results. The finally selected model should efficiently predict specific surface areas of other perovskites for their use in photocatalysis. The new q-RASPR method also appears promising for the prediction of several other property endpoints of interest in materials science.

Automated summary

In this study, a novel quantitative read-across structure-property relationship (q-RASPR) approach was used to model the specific surface area of various perovskites by combining Read-Across (RA) and quantitative structure-property relationship (QSPR). Several machine learning models were developed using different algorithms based on error-based measures and previously selected features. The PLS model was selected as the best predictor for specific surface area, and the new q-RASPR method shows promise for predicting other property endpoints in materials science.