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

摘要

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. 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.