Electrohydrodynamic (EHD) printing has been used in various applications (e.g., sensors, batteries, photonic crystals). Currently, research on studying the relationships between EHD jetting behaviors, material properties, and processing conditions is still challenging due to a large number of parameters, cost, time, and the complex nature of experiments. In this research, we investigated EHD printing behavior using a machine learning (ML)-guided approach to overcome limitations in the experiments. Specifically, we investigated two jetting modes and the size of printed material with a broader range of material properties and processing parameters. We used samples from both literature and our own experiment results with different type of materials. Different ML models have been developed and applied to the data. Our results have shown that ML can navigate a vast parameter search space to predict printing behavior with an accuracy of higher than 95% during EHD printing. Moreover, the results showed that ML models can be used to predict the printing behavior and feather size for new materials. The ML models can guide the investigation of EHD printing and helped us understand the printing behavior in a systematic manner with reduced time, cost, and required experiments.

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y₂O₃ interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies ($E^{Seg}$) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining $E^{Seg}$. Moreover, the segregation behavior at a specific Fe/Y₂O₃ interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y₂O₃ interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.