Defect-Aware Digital Twin

We aim to develop defect-aware predictive digital twins for diverse engineering applications by creating advanced models and simulations that represent defect initiation, evolution, and their impact on material performance.

Reliability for Advanced Packaging with Defects:

We utilize 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

A Machine Learning Framework for Preventing Cracking in Semiconductor Materials

Simulating Defects at 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.

First-order interfacial transformations with a critical point: breaking the symmetry at a symmetric tilt grain boundary’ mechanical properties

Role of disordered bipolar complexions on the sulfur embrittlement of nickel general grain boundaries

Effects of magnesium dopants on grain boundary migration in aluminum-magnesium alloys

Phase-field-lattice Boltzmann method for dendritic growth with melt flow and thermosolutal convection–diffusion

First-principles study of vacancy interaction with grain boundaries of tungsten under tensile strains

Concurrent Atomistic and Continuum Methodology to Simulate Defect Dynamics:

We develop advanced multiscale simulation methods that integrate atomic and continuum scales to predict mechanical and thermal behaviors of defects in microstructurally complex materials with high efficiency and accuracy.

Concurrent atomistic and continuum simulation of strontium titanate

Concurrent atomistic and continuum simulation of bi-crystal strontium titanate with tilt grain boundary

Concurrent atomistic–continuum simulation of polycrystalline strontium titanate’ mechanical properties

Ballistic-diffusive phonon heat transport across grain boundaries

Modeling Defects in 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

Porous microstructures for ion storage in high capacity electrodes based on surface segregation-induced separation

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