Supervised Regression Study for Electron Microscopy Data This study presents a supervised regression model to estimate the growth of Electron Microscopy experimental data for a decade ahead. The study employs the autoregression process model using the best curvefitting that optimizes the level of confidence. Further, the proposed model retains the smallest normalized estimation error. The developed model was competently utilized to estimate the size of Electron Microscopy (EM) data expected to be released within years 2019xe2x80x932028. One EM dataset was used to model and predict the annual growth of released 3DEM. Another EM dataset was used to model and predict the annual number of 3Dimensional EM achieving resolution 10 Ao or better. Indeed, both models used EM data collected in the past 18 years, 2002xe2x80x932018. The experimental results showed that the best curvefitting orders to predict both datasets were AR(5) at 96.8% and AR(6) at 85% for the released 3DEM and 3DEM resolutions datasets, respectively. Therefore, the estimation findings disclose an exponential growing performance in the upcoming evolution for both, the released 3DEM and 3DEM resolutions datasets. However, the evolution rate of the released 3DEM confirms a faster exponential growth.

DataDriven Insights from Predictive Analytics on Heterogeneous Experimental Data of Industrial Magnetic Materials Datadriven methods are becoming increasingly popular in the field of materials science. While most datadriven models are trained on simulation data as it is relatively easier to collect a large amount of data from physicsbased simulations, there are many challenges in applying datadriven methods on experiments: 1) experimental data is usually not clean; and 2) it generally has a greater degree of heterogeneity. In this project, we have developed a datadriven methodology to address these challenges on an industrial magnet dataset, where the goal is to predict magnetic properties (forward models) at different stages of the experimental workflow. The datadriven methodology consists of data cleaning, data preprocessing, feature extraction, and model development using traditional machine learning and deep learning methods to accurately predict magnet properties. In particular, we have developed three different types of predictive models: 1) numerical model using only numerical data containing composition and processing information; 2) image model using image data representing structure information; and 3) combination model using both types of data together. In addition to predictive models, the analysis and comparison of results across the models provide several interesting datadriven insights. Such datadriven analytics has the potential to help guide future experiments and realize the inverse models, which could significantly reduce costs and accelerate the discovery of new magnets with superior properties. The proposed models are already deployed in Toyota Motor Corporation.