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

16 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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), and random forest (RF) regression. Based on the external prediction quality and interpretability, the PLS model 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.

Keywords

q-RASPR
machine learning
Perovskites
Specific surface area
Photocatalysis

Supplementary materials

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Supplementary Materials
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Supplementary Materials SI-1 contains raw data files in Excel format. Supplementary Material SI-2 contains the model equation, applicability domain plots, Regression coefficient plot, Variable importance plot and Randomization plots of the PLS model.
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