Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals-Reference-Cited by-同舟云学术

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Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Published:2024-05-16 Issue:21 Volume:128 Page:8821-8829
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Xiang Xiaofeng¹^ORCID,Soh Dylan²,Dunham Scott³

Affiliation:

1. Molecular Engineering & Sciences Institute, University of Washington, Seattle, Washington 98195, United States

2. Department of Physics, University of Washington, Seattle, Washington 98195, United States

3. Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington 98195, United States

Funder

Solar Energy Technologies Office

Division of Materials Research

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c01124

Reference44 articles.

1. Atomistic models of Cu diffusion in CuInSe2 under variations in composition

2. Xiang, X.; Sommer, D. E.; Gehrke, A.; Dunham, S. T. Coupled process and device modeling of Cu (In, Ga) Se 2 solar cells. 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), 2021; pp 1707–1711.

3. Accurate prediction of defect properties in density functional supercell calculations

4. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

5. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

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