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Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification
[Image: see text] The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain–machine interface also requires computational c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756567/ https://www.ncbi.nlm.nih.gov/pubmed/35036688 http://dx.doi.org/10.1021/acsomega.1c04287 |
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author | Li, Baochen Sun, Haibin Shu, Haonian Wang, Xiaoxue |
author_facet | Li, Baochen Sun, Haibin Shu, Haonian Wang, Xiaoxue |
author_sort | Li, Baochen |
collection | PubMed |
description | [Image: see text] The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain–machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes. |
format | Online Article Text |
id | pubmed-8756567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-87565672022-01-13 Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification Li, Baochen Sun, Haibin Shu, Haonian Wang, Xiaoxue ACS Omega [Image: see text] The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain–machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes. American Chemical Society 2021-12-17 /pmc/articles/PMC8756567/ /pubmed/35036688 http://dx.doi.org/10.1021/acsomega.1c04287 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Li, Baochen Sun, Haibin Shu, Haonian Wang, Xiaoxue Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification |
title | Applying Neuromorphic Computing Simulation in Band
Gap Prediction and Chemical Reaction Classification |
title_full | Applying Neuromorphic Computing Simulation in Band
Gap Prediction and Chemical Reaction Classification |
title_fullStr | Applying Neuromorphic Computing Simulation in Band
Gap Prediction and Chemical Reaction Classification |
title_full_unstemmed | Applying Neuromorphic Computing Simulation in Band
Gap Prediction and Chemical Reaction Classification |
title_short | Applying Neuromorphic Computing Simulation in Band
Gap Prediction and Chemical Reaction Classification |
title_sort | applying neuromorphic computing simulation in band
gap prediction and chemical reaction classification |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756567/ https://www.ncbi.nlm.nih.gov/pubmed/35036688 http://dx.doi.org/10.1021/acsomega.1c04287 |
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