A robust low data solution: Dimension prediction of semiconductor nanorods. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Xiaoli; Xu, Yang; Li, Jiali; Ong, Xuanwei; Ali Ibrahim, Salwa; Buonassisi, Tonio; Wang, Xiaonan
• Is Part Of: Computers & chemical engineering. Volume 150(2021)
• Publication Date: 2021-07
• Subjects: Nanocrystals -- Deep neural network -- SMOTE-REG -- Machine learning -- Low data; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107315
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: SMOTE-REG is proposed for generating samples for small dataset to address regression tasks. SMOTE-REG discovers new samples that match the original data distribution. Deep neural network is applied for regression based on synthetic samples. Experimental results demonstrate significant performance on prediction and inference. The method enables deep learning for solving problems with a small dataset. 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.