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Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion
Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815019/ https://www.ncbi.nlm.nih.gov/pubmed/36620046 http://dx.doi.org/10.3389/fmicb.2022.1059123 |
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author | Allen, Cody Aryal, Shiva Do, Tuyen Gautum, Rishav Hasan, Md Mahmudul Jasthi, Bharat K. Gnimpieba, Etienne Gadhamshetty, Venkataramana |
author_facet | Allen, Cody Aryal, Shiva Do, Tuyen Gautum, Rishav Hasan, Md Mahmudul Jasthi, Bharat K. Gnimpieba, Etienne Gadhamshetty, Venkataramana |
author_sort | Allen, Cody |
collection | PubMed |
description | Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings. |
format | Online Article Text |
id | pubmed-9815019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98150192023-01-06 Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion Allen, Cody Aryal, Shiva Do, Tuyen Gautum, Rishav Hasan, Md Mahmudul Jasthi, Bharat K. Gnimpieba, Etienne Gadhamshetty, Venkataramana Front Microbiol Microbiology Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings. Frontiers Media S.A. 2022-12-22 /pmc/articles/PMC9815019/ /pubmed/36620046 http://dx.doi.org/10.3389/fmicb.2022.1059123 Text en Copyright © 2022 Allen, Aryal, Do, Gautum, Hasan, Jasthi, Gnimpieba and Gadhamshetty. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Allen, Cody Aryal, Shiva Do, Tuyen Gautum, Rishav Hasan, Md Mahmudul Jasthi, Bharat K. Gnimpieba, Etienne Gadhamshetty, Venkataramana Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title | Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title_full | Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title_fullStr | Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title_full_unstemmed | Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title_short | Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion |
title_sort | deep learning strategies for addressing issues with small datasets in 2d materials research: microbial corrosion |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815019/ https://www.ncbi.nlm.nih.gov/pubmed/36620046 http://dx.doi.org/10.3389/fmicb.2022.1059123 |
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