Advanced Energy Materials
Our research harnesses advanced computational techniques to design state-of-the-art materials and structures for diverse energy applications, including nuclear energy and Li-ion batteries.
Advanced Energy Materials:
The research uses advanced computational techniques to design state-of-the-art materials and structures for diverse energy applications, including Li-ion batteries.
Lithium storage mechanisms and electrochemical behavior of a molybdenum disulfide nanoparticle anode
Lithium trapping in germanium nanopores during delithiation process
The NSF Project:
A Machine Learning Framework for Preventing Cracking in Semiconductor Materials
The performance and quality of semiconductor materials are critical to advanced technologies for a wide range of applications. A significant challenge in the production of these materials is the cooling process. During the production phase, semiconductor materials are prone to cracking as they cool. These cracks can lead to failures in the final products, decreased reliability, and higher manufacturing costs. This award supports fundamental research aiming to prevent the formation of cracks during the semiconductor cooling process. The objective of this project is to develop a novel method that integrates machine learning techniques with fundamental principles of mechanics to predict crack formation. This research will enhance production of high-quality semiconductor materials.
The goal of this project is to develop a mechanics-informed machine learning framework to predict and quantify interfacial cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. Recognizing that interfacial defects and residual stresses are critical factors in cracking, the research aim is to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it.