Debojyoti Das is a computational scientist specializing in machine learning applications for chemistry and materials science. He obtained his Ph.D. in Computational Chemistry from Texas Tech University, where his research focused on machine learning–augmented quantum dynamics, molecular property prediction, and data-driven modeling of ion–molecule interactions. His work integrates artificial intelligence, molecular simulations, and electronic structure theory to accelerate the discovery and optimization of functional materials and molecular systems. Currently, he is a postdoctoral researcher at Louisiana State University, working on advanced computational materials projects involving molecular dynamics, many-body potentials, and AI-guided analysis of interfacial phenomena. His broader research interests include generative models for molecular design, reinforcement learning for chemical optimization, and interpretable machine learning for complex physicochemical systems.