Devmin Dorabawila is a PhD student with a strong interest in the intersection of machine learning and photocatalysis. His research explores how advanced deep learning frameworks—such as Graph Neural Networks—can be applied to model the complex structural and electronic dependencies of light-driven catalytic processes. He is particularly interested in leveraging ML to navigate vast chemical spaces to identify more efficient catalysts and predict reactivity and selectivity with high precision. By integrating data-driven insights with chemical principles, he aims to contribute to the discovery of sustainable, next-generation photocatalytic systems.