Preprocessed data, source codes, and relevant sample calculations for a Fourier-transformed feature engineering design for predicting ternary perovskite properties by (Conv2D-SVM)

Link	Link to dataset
Author	Search for: Chenebuah, Ericsson Tetteh¹ORCID identifier: https://orcid.org/0000-0003-2878-3484; Search for: Nganbe, Michel ORCID identifier: https://orcid.org/0000-0002-2240-9099; Search for: Tchagang, Alain Beaudelaire¹
Name affiliation	National Research Council of Canada. Digital Technologies
Funder	Search for: National Research Council Canada; Search for: Natural Sciences and Engineering Research Council of Canada
Format	Software, multimedia, Dataset
Subject	perovskite; Fourier transformation; convolutional neural network; support vector machine; energy bandgap
Abstract	A feature engineering approach for modeling ternary perovskite target properties by Fourier transforming direct features into the periodic reciprocal crystal lattice
Publication date	2022-12-11
Publisher	National Research Council of Canada
Copyright statement	© National Research Council of Canada
Related publication	Chenebuah, Ericsson Tetteh, Michel Nganbe, and Alain Beaudelaire Tchagang. ‘A Fourier-Transformed Feature Engineering Design for Predicting Ternary Perovskite Properties by Coupling a Two-Dimensional Convolutional Neural Network with a Support Vector Machine (Conv2D-SVM)’. Materials Research Express 10, no. 2 (1 February 2023): 026301. https://doi.org/10.1088/2053-1591/acb683.
Language	English
Export citation	Export as RIS
Collection	NRC Research Data
Record identifier	8d959c34-bb9b-4b71-8747-0888c96db647
Record created	2023-02-06
Record modified	2023-02-06