A robust low data solution: Dimension prediction of semiconductor nanorods
Name
2010.14111.pdf
Description
Submitted version
Size
1.04 MB
Format
Unknown
Checksum (MD5)
3a4f0ebf993662244a6bec1a87d5aee7
Author(s)
Liu, Xiaoli
Xu, Yang
Li, Jiali
Ong, Xuanwei
Ali Ibrahim, Salwa
Buonassisi, Tonio
Wang, Xiaonan
Date Issued
April 2021
Journal
Computers and Chemical Engineering
Publisher
Elsevier BV
Citation
Liu, Xiaoli, Xu, Yang, Li, Jiali, Ong, Xuanwei, Ali Ibrahim, Salwa et al. 2021. "A robust low data solution: Dimension prediction of semiconductor nanorods." Computers and Chemical Engineering, 150.
Version
Original manuscript
Abstract
Precise control over dimension of nanocrystals is critical to tune the properties for various applications. However, the traditional control through experimental optimization is slow, tedious and time consuming. Herein a robust deep neural network-based regression algorithm has been developed for precise prediction of length, width, and aspect ratios of semiconductor nanorods (NRs). Given there is limited experimental data available (28 samples), a Synthetic Minority Oversampling Technique for regression (SMOTE-REG) is employed first for data generation. Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution. The prediction model is further validated with additional experimental data, showing accurate prediction results. Additionally, Local Interpretable Model-Agnostic Explanations (LIME) is used to interpret the weight for each sample, corresponding to its importance towards the target dimension, which is well validated by experimental observations.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.COMPCHEMENG.2021.107315
