Changhwan Oh

Changhwan Oh is a graduate student in materials science and engineering working at the intersection of computational chemistry and machine learning to accelerate the design of materials for greenhouse gas capture. His research focuses on metal-organic frameworks (MOFs), a class of porous materials designed to selectively store carbon dioxide and methane. A central challenge is designing MOFs that are not only highly adsorptive but also exhibit diverse forms of stability, especially under industrial conditions. As a MathWorks Fellow, Changhwan will develop multi-objective optimization workflows that integrate machine learning, high-throughput screening, and quantum simulations to discover next-generation MOFs for carbon capture in humid environments. He utilizes MATLAB for deep learning, feature engineering, data parsing, and optimization, leveraging the Statistics and Machine Learning Toolbox, Deep Learning Toolbox, and Global Optimization Toolbox. By bridging chemical physics with data science, Changhwan’s work could enable scalable climate solutions and provide researchers with powerful, user-friendly tools for materials discovery.