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- Publisher Website: 10.4208/nmtma.OA-2020-0032
- Scopus: eid_2-s2.0-85091458054
- WOS: WOS:000540556500004
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Article: A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains
Title | A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains |
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Authors | |
Keywords | Exciton diffusion length deep learning Quasi-Monte Carlo sampling diffusion equation |
Issue Date | 2020 |
Publisher | Global Science Press. The Journal's web site is located at http://www.global-sci.org/nmtma/ |
Citation | Numerical Mathematics: Theory, Methods and Applications, 2020, v. 13, p. 908-927 How to Cite? |
Abstract | Exciton diffusion plays a vital role in the function of many organic semiconducting opto-electronic devices, where an accurate description requires precise control of heterojunctions. This poses a challenging problem because the parameterization of heterojunctions in high-dimensional random space is far beyond the capability of classical simulation tools. Here, we develop a novel method based on Quasi-Monte Carlo sampling to generate the training data set and deep neural network to extract a function for exciton diffusion length on surface roughness with high accuracy and unprecedented efficiency, yielding an abundance of information over the entire parameter space. Our method provides a new strategy to analyze the impact of interfacial ordering on exciton diffusion and is expected to assist experimental design with tailored opto-electronic functionalities. |
Persistent Identifier | http://hdl.handle.net/10722/285095 |
ISSN | 2023 Impact Factor: 1.9 2023 SCImago Journal Rankings: 0.670 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lyu, L | - |
dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Chen, J | - |
dc.date.accessioned | 2020-08-07T09:06:40Z | - |
dc.date.available | 2020-08-07T09:06:40Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Numerical Mathematics: Theory, Methods and Applications, 2020, v. 13, p. 908-927 | - |
dc.identifier.issn | 1004-8979 | - |
dc.identifier.uri | http://hdl.handle.net/10722/285095 | - |
dc.description.abstract | Exciton diffusion plays a vital role in the function of many organic semiconducting opto-electronic devices, where an accurate description requires precise control of heterojunctions. This poses a challenging problem because the parameterization of heterojunctions in high-dimensional random space is far beyond the capability of classical simulation tools. Here, we develop a novel method based on Quasi-Monte Carlo sampling to generate the training data set and deep neural network to extract a function for exciton diffusion length on surface roughness with high accuracy and unprecedented efficiency, yielding an abundance of information over the entire parameter space. Our method provides a new strategy to analyze the impact of interfacial ordering on exciton diffusion and is expected to assist experimental design with tailored opto-electronic functionalities. | - |
dc.language | eng | - |
dc.publisher | Global Science Press. The Journal's web site is located at http://www.global-sci.org/nmtma/ | - |
dc.relation.ispartof | Numerical Mathematics: Theory, Methods and Applications | - |
dc.subject | Exciton diffusion length | - |
dc.subject | deep learning | - |
dc.subject | Quasi-Monte Carlo sampling | - |
dc.subject | diffusion equation | - |
dc.title | A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains | - |
dc.type | Article | - |
dc.identifier.email | Zhang, Z: zhangzw@hku.hk | - |
dc.identifier.authority | Zhang, Z=rp02087 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.4208/nmtma.OA-2020-0032 | - |
dc.identifier.scopus | eid_2-s2.0-85091458054 | - |
dc.identifier.hkuros | 311586 | - |
dc.identifier.volume | 13 | - |
dc.identifier.spage | 908 | - |
dc.identifier.epage | 927 | - |
dc.identifier.isi | WOS:000540556500004 | - |
dc.publisher.place | [Hong Kong] | - |
dc.identifier.issnl | 1004-8979 | - |