Document Type
Article
Abstract
Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA’ MH6 semiconductors in the F-43m space group including BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, and ScZnMnH6. Previous research reported that five AA ‘IrH6 semiconductors with the same space group were synthesized. Our research shows that AA MnH6 and NaYRuH6 semiconductors have considerably different properties compared to the rest of the AA MH6 semiconductors. Based on the accurate hybrid functional calculations, AA MH6 semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH6 and KNaNiH6 are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps.
Digital Object Identifier (DOI)
Publication Info
Published in NPJ Computational Materials, Volume 8, 2022.
Rights
© The Author(s) 2022
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
APA Citation
Siriwardane, E. M. D., Zhao, Y., Perera, I., & Hu, J. (2022). Generative design of stable semiconductor materials using deep learning and density functional theory. NPJ Computational Materials, 8. https://doi.org/10.1038/s41524-022-00850-3
