Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms...

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Autores principales:	Marco Fronzi, Olexandr Isayev, David A. Winkler, Joseph G. Shapter, Amanda V. Ellis, Peter C. Sherrell, Nick A. Shepelin, Alexander Corletto, Michael J. Ford
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	active learning bandgaps bilayers 2D heterostructures Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
Acceso en línea:	https://doaj.org/article/e41b429baf3547a4b1f34b7a6c481518
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id	oai:doaj.org-article:e41b429baf3547a4b1f34b7a6c481518
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spelling	oai:doaj.org-article:e41b429baf3547a4b1f34b7a6c4815182021-11-23T07:58:49ZActive Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures2640-456710.1002/aisy.202100080https://doaj.org/article/e41b429baf3547a4b1f34b7a6c4815182021-11-01T00:00:00Zhttps://doi.org/10.1002/aisy.202100080https://doaj.org/toc/2640-4567The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.Marco FronziOlexandr IsayevDavid A. WinklerJoseph G. ShapterAmanda V. EllisPeter C. SherrellNick A. ShepelinAlexander CorlettoMichael J. FordWileyarticleactive learningbandgapsbilayers2D heterostructuresComputer engineering. Computer hardwareTK7885-7895Control engineering systems. Automatic machinery (General)TJ212-225ENAdvanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	active learning bandgaps bilayers 2D heterostructures Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
spellingShingle	active learning bandgaps bilayers 2D heterostructures Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 Marco Fronzi Olexandr Isayev David A. Winkler Joseph G. Shapter Amanda V. Ellis Peter C. Sherrell Nick A. Shepelin Alexander Corletto Michael J. Ford Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
description	The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.
format	article
author	Marco Fronzi Olexandr Isayev David A. Winkler Joseph G. Shapter Amanda V. Ellis Peter C. Sherrell Nick A. Shepelin Alexander Corletto Michael J. Ford
author_facet	Marco Fronzi Olexandr Isayev David A. Winkler Joseph G. Shapter Amanda V. Ellis Peter C. Sherrell Nick A. Shepelin Alexander Corletto Michael J. Ford
author_sort	Marco Fronzi
title	Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
title_short	Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
title_full	Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
title_fullStr	Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
title_full_unstemmed	Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
title_sort	active learning in bayesian neural networks for bandgap predictions of novel van der waals heterostructures
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/e41b429baf3547a4b1f34b7a6c481518
work_keys_str_mv	AT marcofronzi activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT olexandrisayev activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT davidawinkler activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT josephgshapter activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT amandavellis activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT petercsherrell activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT nickashepelin activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT alexandercorletto activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures AT michaeljford activelearninginbayesianneuralnetworksforbandgappredictionsofnovelvanderwaalsheterostructures
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Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

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