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Title: Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”

Abstract

To close the feedback loop between artificial intellegence-controlled materials synthesis and characterization, material functionality must be rapidly tested. A platform for high-throughput multifunctional materials characterization is developed using a quartz crystal microbalance with auxiliary in-plane electrodes and a custom gas/vapor flow cell, enabling simultaneous scanning probe microscopy and electrical, optical, gravimetric, and viscoelastic characterization on the same film under controlled environment. The lab-on-a-crystal in situ multifunctional output allows direct correlations between the gravimetric/viscoelastic, electrical, and optical responses of polymer film in response to environment. Finally, when multiple film properties are used to augment the training set for machine learning regression, prediction of material response to the environment improves by a factor of 13 when <5% of the total dataset is used for model training.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1608219
Alternate Identifier(s):
OSTI ID: 1592814
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Functional Materials
Additional Journal Information:
Journal Volume: 30; Journal Issue: 10; Journal ID: ISSN 1616-301X
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; gravimetric; impedance; machine learning; multifunctional materials; PEDIT:PSS; QCM; viscoelastic

Citation Formats

Muckley, Eric S., Collins, Liam, Srijanto, Bernadeta, and Ivanov, Ilia N. Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”. United States: N. p., 2020. Web. https://doi.org/10.1002/adfm.201908010.
Muckley, Eric S., Collins, Liam, Srijanto, Bernadeta, & Ivanov, Ilia N. Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”. United States. https://doi.org/10.1002/adfm.201908010
Muckley, Eric S., Collins, Liam, Srijanto, Bernadeta, and Ivanov, Ilia N. Tue . "Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”". United States. https://doi.org/10.1002/adfm.201908010. https://www.osti.gov/servlets/purl/1608219.
@article{osti_1608219,
title = {Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”},
author = {Muckley, Eric S. and Collins, Liam and Srijanto, Bernadeta and Ivanov, Ilia N.},
abstractNote = {To close the feedback loop between artificial intellegence-controlled materials synthesis and characterization, material functionality must be rapidly tested. A platform for high-throughput multifunctional materials characterization is developed using a quartz crystal microbalance with auxiliary in-plane electrodes and a custom gas/vapor flow cell, enabling simultaneous scanning probe microscopy and electrical, optical, gravimetric, and viscoelastic characterization on the same film under controlled environment. The lab-on-a-crystal in situ multifunctional output allows direct correlations between the gravimetric/viscoelastic, electrical, and optical responses of polymer film in response to environment. Finally, when multiple film properties are used to augment the training set for machine learning regression, prediction of material response to the environment improves by a factor of 13 when <5% of the total dataset is used for model training.},
doi = {10.1002/adfm.201908010},
journal = {Advanced Functional Materials},
number = 10,
volume = 30,
place = {United States},
year = {2020},
month = {1}
}

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