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.

精确控制纳米晶体的尺寸对于调整各种应用的性能至关重要。但是，通过实验优化进行的传统控制速度慢，繁琐且耗时。在本文中，已经开发了基于鲁棒深度神经网络的回归算法，用于精确预测半导体纳米棒（NRs）的长度，宽度和纵横比。鉴于只能提供有限的实验数据（28个样本），因此首先采用了用于回归的综合少数族裔过采样技术（SMOTE-REG）进行数据生成。深度神经网络被进一步用于开发回归模型，该模型展示了对原始数据和生成数据具有相似分布的良好预测。该预测模型还通过其他实验数据进行了验证，从而显示出准确的预测结果。