Molecular Simulation

Molecular Simulation and Computational Chemistry

While machine learning provides powerful tools for pattern recognition and prediction, physics-based simulations remain essential for understanding the fundamental behavior of molecules and materials. In our group, we integrate molecular simulation and computational chemistry with machine learning to create hybrid approaches for materials discovery.

Our research includes quantum chemical calculations, molecular dynamics simulations, and other atomistic modeling techniques that provide detailed insights into molecular structure, electronic properties, and reaction mechanisms. These simulations help validate machine learning predictions and provide physically grounded data that can improve model reliability. By combining data-driven models with physics-based simulations, we aim to develop more accurate, interpretable, and robust approaches for designing next-generation molecules and materials.

Selected papers

[1]  J. Guo#, J. W. Baek#, Y. Kang#, D. Kim#, C. Park, J. Woo, T. Zou, E. Shin, Q. Wang, Y. Noh, S. Park,  J. Kim*, Yun. Li*, I. D. Kim*, K. Kang*

Molecular Sensitizer-Loaded Monolayer Organic Semiconductors for High-Performance H2S Sensors

ACS Nano,  2025

[2]  D. W. Kim#, H. Mun#, Y. Kang, W. G. Kim, D. Ahn, S. Y. Yun, J. A. Han,  D. H. Lee, T. Lee, K. Jeong, J. Kim, S. G. Im*, Y. K. Choi*

Synthesis of stretchable triboelectric material with strain-compensating ability using gradient interpenetrating polymer networks

Energy & Environmental Science,  2025