Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Allen, Cody, Aryal, Shiva, Do, Tuyen, Gautum, Rishav, Hasan, Md Mahmudul, Jasthi, Bharat K., Gnimpieba, Etienne, Gadhamshetty, Venkataramana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784864263861436416
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
work_keys_str_mv AT allencody deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT aryalshiva deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT dotuyen deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT gautumrishav deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT hasanmdmahmudul deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT jasthibharatk deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT gnimpiebaetienne deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion
AT gadhamshettyvenkataramana deeplearningstrategiesforaddressingissueswithsmalldatasetsin2dmaterialsresearchmicrobialcorrosion