Computational Modeling and Simulation of Materials
Material Reliability for Advanced Packaging:
We utilize atomic-level computational simulations and machine learning techniques to model failure mechanisms in advanced packaging, aiming to improve the reliability predictions and performance of microelectronic devices.
Atomistic Modeling of Interfacial Cracking in Copper-To-Copper Direct Bonding
Modeling of Microstructural Evolution within TSVs Using Atomistic Simulations
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
Interfaces in Materials:
We explore the impact of various interfaces on material properties to uncover fundamental mechanisms, empowering us to design advanced materials through strategic interfacial engineering.
Effects of magnesium dopants on grain boundary migration in aluminum-magnesium alloys
Concurrent Atomistic and Continuum Methodology Development:
Concurrent atomistic and continuum simulation of strontium titanate
Ballistic-diffusive phonon heat transport across grain boundaries
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