Computational Energy Materials

Research

AI/ML for Energy & Semiconductor Materials

Our research group focuses on designing next-generation materials and devices enabled by 2D semiconductors, van der Waals heterostructures, quantum dots, catalytic systems, and advanced battery materials. We aim to accelerate the discovery of materials for energy, electronics, and quantum technologies, improving energy storage performance, reducing development time and cost, and maximizing overall efficiency.

In addition, we investigate heat dissipation and thermal transport in integrated circuits (ICs) and advanced packaging systems, addressing critical challenges in device reliability and performance at the nanoscale.

By integrating predictive simulations, AI/ML modeling, and advanced computational techniques, we strive to create materials and systems that bridge fundamental science with real-world applications, promoting both technological innovation and long-term sustainability.

#DFT; #AIMD; #Matlab; #MD; AI/ML; #Heat transfer simulation

#Energy conversion; #Energy storage; #2D Semiconductor

Catalytic Materials

Original CEM Lab photograph or research figure

#SingleAtomCatalyst

#DualAtomCatalyst

#2DMaterials

#HeterogeneousIntegration

#ORR #OER #HER #CO2RR #NO3RR

#EnergyConversion

Metal Ion Batteries

# Lithium-ion Battery (LIB)

# Lithium–sulfur Battery (Li-S)

# Graphene-based anode materials

# Solid-electrolyte interphase (SEI)

# Rechargeable batteries

AI/ML for Energy & Semiconductor Materials

# Al/ML models

# High through calculation

# Energy materials (Catalysts, batteries)

# Semiconductors

Heat Dissipation

# Heat dissipation simulation

# IC & Packaging devices

# COMSOL, ANSYS, MATLAB