  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) has been employed
for the first time 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
variable, which corresponds to its importance towards the target dimension,
which is approved to be well correlated well with experimental observations.
