Predicting band gaps of ABN₃ perovskites: an account from machine learning and first-principle DFT studies.

ABSTRACT: The present paper is primarily focused on predicting the band gaps of nitride perovskites from machine learning (ML) models. The ML models have been framed from the feature descriptors and band gap values of 1563 inorganic nitride perovskites having formation energies <-0.026 eV and band gaps ranging from ∼1.0 to 3.1 eV. Four supervised ML models such as multi-layer perceptron (MLP), gradient boosted decision tree (GBDT), support vector regression (SVR) and random forest regression (RFR) have been considered to predict the band gaps of the said systems. The accuracy of each model has been tested from mean absolute error, root-mean-square error and determination coefficient R² values. The bivariate plots between the predicted and input band gaps of the compounds for both t

SUBMITTER: Ghosh S 

PROVIDER: S-EPMC10877485 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature