logo
ResearchBunny Logo
Generative design of stable semiconductor materials using deep learning and density functional theory

Engineering and Technology

Generative design of stable semiconductor materials using deep learning and density functional theory

E. M. D. Siriwardane, Y. Zhao, et al.

Explore groundbreaking advancements in semiconductor research with work conducted by Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera, and Jianjun Hu. This study unveils a novel computational pipeline integrating generative adversarial networks and high-throughput calculations, leading to the discovery of 12 stable AA'MH6 semiconductors boasting remarkable wide-bandgap properties.

00:00
00:00
Playback language: English
Abstract
This research developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GANs), classifiers, and high-throughput first-principles calculations. Using CubicGAN, a GAN-based algorithm for generating cubic materials, and a classifier to screen semiconductors, the study identified 12 stable AA'MH6 semiconductors in the F-43m space group. These semiconductors were found to be wide-bandgap, with BaSrZnH6 and KNaNiH6 exhibiting direct bandgaps while others showed indirect bandgaps. The research highlights the significant differences in properties between AA'MnH6 and NaYRuH6 compared to other AA'MH6 semiconductors.
Publisher
npj Computational Materials
Published On
Jan 31, 2022
Authors
Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera, Jianjun Hu
Tags
semiconductors
generative adversarial networks
CubicGAN
stable materials
bandgap properties
high-throughput calculations
AA'MH6

Related Publications

Explore these studies to deepen your understanding of the subject.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
Chemistry

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

S. Chen, T. Lin, et al.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
Medicine and Health

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

X. Xue, S. Chinnaperumal, et al.

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
Medicine and Health

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

J. Zhang, F. Zhong, et al.

Nondestructive monitoring of annealing and chemical-mechanical planarization behavior using ellipsometry and deep learning
Engineering and Technology

Nondestructive monitoring of annealing and chemical-mechanical planarization behavior using ellipsometry and deep learning

Q. Sun, D. Yang, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny