Chemical kinetic modeling is an indispensable tool, providing profound insights into the intricate molecular events unfolding within complex chemical reactions. By elucidating detailed atomistic mechanisms, it facilitates the design of novel materials, experiments, and industrial processes. These reactions encompass a multitude of reacting species, their complex interactions across different phases, and intricate reaction pathways characterized by essential chemical and physical parameters, including rate constants and diffusivity. Nevertheless, constructing kinetic models capable of accurately predicting and interpreting the underlying chemistry of real systems presents a formidable challenge due to the vastness of chemical space. The substantial computational costs primarily stem from the scale of systems and the need for interatomic potentials that ensure first-principle accuracy in describing molecular-level processes. The evolution of machine learning techniques has expanded the boundaries of system and time scales in theoretical chemistry, a breakthrough previously challenging to achieve. Among various machine learning-assisted modeling approaches, machine-learning interatomic potentials and data-driven models have gained considerable attention due to their efficiency and chemical interpretability. Machine learning potentials combine the efficiency of empirical potentials with the precision of first-principle theories, breaking the conventional speed-accuracy trade-off. Meanwhile, data-driven models extract essential chemical information from data to enhance partial theoretical structures, resulting in efficient and interpretable models. In this dissertation, our primary focus lies in the application of advanced machine learning techniques to tackle complex chemical problems and interpret the underlying kinetics. We introduce a methodology and a platform designed to expedite transition state calculations. Furthermore, we present chemistry-informed data-driven kinetic models applied to liquid-liquid extraction, a process characterized by numerous kinetic parameters exhibiting real-time dynamics. Furthermore, we examine the atomistic mechanisms underlying surface segregation in cuprinickel (CuNi) alloy systems using multiscale simulations. This highlights the significant potential of efficient kinetic tools in unraveling the chemistry behind such phenomena.Computational Science, Engineering, and Mathematic
