High-throughput exploration of stable semiconductors using deep learning and density functional theory

Semiconductors can lead to new applications and technological innovations. In this work, we developed a computational pipeline to discover new semiconductors by combining deep learning and high-throughput first-principles calculations. We used a random strategy combined with group and graph theory t...

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Bibliographic Details
Published in:Materials science in semiconductor processing 2025-03, Vol.188, p.109150, Article 109150
Main Authors: Min, Gege, Wei, Wenxu, Fan, Qingyang, Wan, Teng, Ye, Ming, Yun, Sining
Format: Article
Language:English
Subjects:
First-principles calculations
Graph convolutional neural network
High-throughput screening
Semiconductor
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Summary:Semiconductors can lead to new applications and technological innovations. In this work, we developed a computational pipeline to discover new semiconductors by combining deep learning and high-throughput first-principles calculations. We used a random strategy combined with group and graph theory to generate initial boron nitride polymorphs and developed a classifier based on graph convolutional neural network to screen semiconductors and study their stability. We found 26 new stable boron nitride polymorphs in Pc phase, of which 3 are direct bandgap semiconductors, and 10 are quasi-direct bandgap semiconductors. This discovery not only expands the library of known semiconductor materials but also provides potential candidates for high-performance electronic and optoelectronic devices, paving the way for future technological advancements. [Display omitted]
ISSN:1369-8001
DOI:10.1016/j.mssp.2024.109150