Our research group is dedicated to the advancement of energy and polymer materials through innovative methods. Specifically, we employ a systematic integration of Density Functional Theory (DFT), Kinetic Monte Carlo (kMC), and data-driven Long Short-Term Memory (LSTM) techniques to investigate and analyze their properties. By adopting this multifaceted approach, our primary objective is to enhance our understanding of various materials, thereby paving the way for future breakthroughs in electrochemical catalysis, battery technology, and polymer systems.
Literature:
ACS Catal. 2022, 12, 24, 15609–15617 (DFT-kMC Analysis for Identifying Novel Bimetallic Electrocatalysts for Enhanced NRR Performance by Suppressing HER at Ambient Conditions Via Active-Site Separation)
ACS Catal. 2023, 13, XXX, 8336–8346 (Investigating High-performance Non-Precious Transition Metal Oxide Catalysts for Nitrogen Reduction Reaction: A Multifaceted DFT-kMC-LSTM Approach)