Predicting the photon energy of quasi-2D lead halide perovskites from the precursor composition through machine learning

Wang, Wei; Li, Yueqiao; Zou, Ang; Shi, Hong; Huang, Xiwei; Li, Yaoyao; Qiao, Bo; Wei, Dong; Zhao, Suling; Xu, Zheng; Song, Dandan

doi:10.1039/d2na00052k

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…Zhang and Xu [19] worked on predicting the oxygen ionic conductivities of perovskites of type ABO 3 using a Gaussian process regression model. Wang et al [20]. utilized Random Forest (RF) to predict the photon energies associated with quasi-2D perovskite materials and their precursors.…”

Section: Introductionmentioning

confidence: 99%

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

Banerjee

Gajewicz-Skretna

Roy

2023

Molecular Informatics

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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.

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Section: Introductionmentioning

confidence: 99%