Authors: Arkaprava Banerjee | Agnieszka Gajewicz | Kunal Roy
Source: Molecular Informatics (article, 2023-01-09)
Field: Materials Science — Materials Chemistry

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 (n<sub>Training</sub> = 38, n<sub>Test</sub> = 12, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msubsup><mml:mi>R</mml:mi> <mml:mrow><mml:mi>T</mml:mi> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msubsup> <mml:annotation>${{R}_{Train}^{2}}$</mml:annotation> </mml:semantics> </mml:math> =0.737, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msubsup><mml:mi>Q</mml:mi> <mml:mrow><mml:mi>L</mml:mi> <mml:mi>O</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>637</mml:mn> <mml:mo>,</mml:mo> <mml:mspace></mml:mspace> <mml:msubsup><mml:mi>R</mml:mi> <mml:mrow><mml:mi>T</mml:mi> <mml:mi>e</mml:mi> <mml:mi>s</mml:mi> <mml:mi>t</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>898</mml:mn> <mml:mo>,</mml:mo> <mml:mspace></mml:mspace> <mml:mspace></mml:mspace> <mml:msubsup><mml:mi>Q</mml:mi> <mml:mrow><mml:mi>F</mml:mi> <mml:mn>1</mml:mn> <mml:mfenced><mml:mi>T</mml:mi> <mml:mi>e</mml:mi> <mml:mi>s</mml:mi> <mml:mi>t</mml:mi></mml:mfenced> </mml:mrow> <mml:mn>2</mml:mn></mml:msubsup> <mml:mrow><mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>901</mml:mn> <mml:mo>)</mml:mo></mml:mrow> </mml:mrow> <mml:annotation>${{Q}_{LOO}^{2}=0.637,\ {R}_{Test}^{2}=0.898,{\rm \ }\ {Q}_{F1\left(Test\right)}^{2}=0.901)}$</mml:annotation> </mml:semantics> </mml:math> 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. Surface (topology) ; Cheminformatics ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors