Deep learning-enabled prediction of 2D material breakdown

10.1088/1361-6528/abd655

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Main Authors:	Huan, Yan Qi, Liu, Yincheng, Goh, Kuan Eng Johnson, Wong, Swee Liang, Lau, Chit Siong
Other Authors:	PHYSICS
Format:	Article
Language:	English
Published:	IOP PUBLISHING LTD 2021
Subjects:	Science & Technology Technology Physical Sciences Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Science & Technology - Other Topics Materials Science Physics machine learning convolutional neural network long short-term memory electric breakdown transition metal dichalcogenides molybdenum disulfide field-effect transistor FIELD-EFFECT TRANSISTORS MONOLAYER MOS2 ELECTRICAL BREAKDOWN INTEGRATED-CIRCUITS GRAPHENE
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/202309
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-202309
record_format	dspace
spelling	sg-nus-scholar.10635-2023092024-04-17T10:05:15Z Deep learning-enabled prediction of 2D material breakdown Huan, Yan Qi Liu, Yincheng Goh, Kuan Eng Johnson Wong, Swee Liang Lau, Chit Siong PHYSICS Science & Technology Technology Physical Sciences Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Science & Technology - Other Topics Materials Science Physics machine learning convolutional neural network long short-term memory electric breakdown transition metal dichalcogenides molybdenum disulfide field-effect transistor FIELD-EFFECT TRANSISTORS MONOLAYER MOS2 ELECTRICAL BREAKDOWN INTEGRATED-CIRCUITS GRAPHENE 10.1088/1361-6528/abd655 NANOTECHNOLOGY 32 26 2021-10-08T00:35:23Z 2021-10-08T00:35:23Z 2021-06-25 2021-10-07T15:46:14Z Article Huan, Yan Qi, Liu, Yincheng, Goh, Kuan Eng Johnson, Wong, Swee Liang, Lau, Chit Siong (2021-06-25). Deep learning-enabled prediction of 2D material breakdown. NANOTECHNOLOGY 32 (26). ScholarBank@NUS Repository. https://doi.org/10.1088/1361-6528/abd655 09574484 13616528 https://scholarbank.nus.edu.sg/handle/10635/202309 en IOP PUBLISHING LTD Elements
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
language	English
topic	Science & Technology Technology Physical Sciences Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Science & Technology - Other Topics Materials Science Physics machine learning convolutional neural network long short-term memory electric breakdown transition metal dichalcogenides molybdenum disulfide field-effect transistor FIELD-EFFECT TRANSISTORS MONOLAYER MOS2 ELECTRICAL BREAKDOWN INTEGRATED-CIRCUITS GRAPHENE
spellingShingle	Science & Technology Technology Physical Sciences Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Science & Technology - Other Topics Materials Science Physics machine learning convolutional neural network long short-term memory electric breakdown transition metal dichalcogenides molybdenum disulfide field-effect transistor FIELD-EFFECT TRANSISTORS MONOLAYER MOS2 ELECTRICAL BREAKDOWN INTEGRATED-CIRCUITS GRAPHENE Huan, Yan Qi Liu, Yincheng Goh, Kuan Eng Johnson Wong, Swee Liang Lau, Chit Siong Deep learning-enabled prediction of 2D material breakdown
description	10.1088/1361-6528/abd655
author2	PHYSICS
author_facet	PHYSICS Huan, Yan Qi Liu, Yincheng Goh, Kuan Eng Johnson Wong, Swee Liang Lau, Chit Siong
format	Article
author	Huan, Yan Qi Liu, Yincheng Goh, Kuan Eng Johnson Wong, Swee Liang Lau, Chit Siong
author_sort	Huan, Yan Qi
title	Deep learning-enabled prediction of 2D material breakdown
title_short	Deep learning-enabled prediction of 2D material breakdown
title_full	Deep learning-enabled prediction of 2D material breakdown
title_fullStr	Deep learning-enabled prediction of 2D material breakdown
title_full_unstemmed	Deep learning-enabled prediction of 2D material breakdown
title_sort	deep learning-enabled prediction of 2d material breakdown
publisher	IOP PUBLISHING LTD
publishDate	2021
url	https://scholarbank.nus.edu.sg/handle/10635/202309
_version_	1800915026474696704

Deep learning-enabled prediction of 2D material breakdown

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