Machine learning enabled characterization of charge transport in organic semiconducting devices

While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that the...

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Main Author: Lakshminarayanan, Madhavkrishnan
Other Authors: Leong Wei Lin
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/161878
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1618782022-10-04T01:04:35Z Machine learning enabled characterization of charge transport in organic semiconducting devices Lakshminarayanan, Madhavkrishnan Leong Wei Lin School of Electrical and Electronic Engineering wlleong@ntu.edu.sg Engineering::Electrical and electronic engineering::Semiconductors Engineering::Materials::Organic/Polymer electronics While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that there is no unified approach to characterize transport in these materials. With machine learning algorithms that learn input-output relationships, device data can aid in the determination of transport parameters, offering physical insights about transport and material design. Furthermore, these techniques can aid in improving device figure-of-merit by screening for novel materials. In this thesis, we examine the extent of applicability of conventional theories on transport in semiconducting devices. We demonstrate different data-driven and physics-inspired approaches, independent of conventional theories, to characterise transport using fewer parameters. We also study structure at the molecular level by employing machine learning methods to predict thermoelectric properties of single molecular junctions. Doctor of Philosophy 2022-09-23T00:55:10Z 2022-09-23T00:55:10Z 2022 Thesis-Doctor of Philosophy Lakshminarayanan, M. (2022). Machine learning enabled characterization of charge transport in organic semiconducting devices. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161878 https://hdl.handle.net/10356/161878 10.32657/10356/161878 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Semiconductors
Engineering::Materials::Organic/Polymer electronics
spellingShingle Engineering::Electrical and electronic engineering::Semiconductors
Engineering::Materials::Organic/Polymer electronics
Lakshminarayanan, Madhavkrishnan
Machine learning enabled characterization of charge transport in organic semiconducting devices
description While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that there is no unified approach to characterize transport in these materials. With machine learning algorithms that learn input-output relationships, device data can aid in the determination of transport parameters, offering physical insights about transport and material design. Furthermore, these techniques can aid in improving device figure-of-merit by screening for novel materials. In this thesis, we examine the extent of applicability of conventional theories on transport in semiconducting devices. We demonstrate different data-driven and physics-inspired approaches, independent of conventional theories, to characterise transport using fewer parameters. We also study structure at the molecular level by employing machine learning methods to predict thermoelectric properties of single molecular junctions.
author2 Leong Wei Lin
author_facet Leong Wei Lin
Lakshminarayanan, Madhavkrishnan
format Thesis-Doctor of Philosophy
author Lakshminarayanan, Madhavkrishnan
author_sort Lakshminarayanan, Madhavkrishnan
title Machine learning enabled characterization of charge transport in organic semiconducting devices
title_short Machine learning enabled characterization of charge transport in organic semiconducting devices
title_full Machine learning enabled characterization of charge transport in organic semiconducting devices
title_fullStr Machine learning enabled characterization of charge transport in organic semiconducting devices
title_full_unstemmed Machine learning enabled characterization of charge transport in organic semiconducting devices
title_sort machine learning enabled characterization of charge transport in organic semiconducting devices
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/161878
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