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

From National Research Council Canada

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AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0003-2878-3484; Search for: ORCID identifier: https://orcid.org/0000-0002-2240-9099; Search for: 1
Affiliation
  1. National Research Council of Canada. Digital Technologies
FunderSearch for: National Research Council Canada; Search for: Natural Sciences and Engineering Research Council of Canada
FormatSoftware, multimedia, Dataset
Subjectperovskite; 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
PublisherNational Research Council of Canada
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  • © 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.
LanguageEnglish
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CollectionNRC Research Data
Record identifier8d959c34-bb9b-4b71-8747-0888c96db647
Record created2023-02-06
Record modified2023-02-06
Date modified: