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.
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (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. doi: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 = {Tue Jan 21 00:00:00 EST 2020},
month = {Tue Jan 21 00:00:00 EST 2020}
}
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