Finite Rate Chemistry

Finite-rate chemistry focuses on accurately modeling chemical reactions where reaction speeds significantly impact system behavior, a departure from the assumption of instantaneous reactions. Current research emphasizes developing efficient computational methods to handle the complexity of these systems, employing machine learning techniques like neural networks (including physics-informed neural networks and generative adversarial networks) and deep reinforcement learning to predict reaction rates and solve reaction-diffusion equations, often with a focus on improving speed and accuracy compared to traditional methods. These advancements are crucial for diverse applications, ranging from atmospheric chemistry and combustion modeling to astrophysical simulations and drug design, enabling more accurate and computationally feasible simulations of complex chemical processes.