The Analysis of Dissociation Process of Carbonic Acid in Aqueous by Using Molecular Dynamics with Machine Learning Potential

Abstract

For example， dissolved carbon dioxide and carbonic acid in aqueous solution are important compounds for life activities， serving as a buffer system for pH regulation in the body. Theoretical calculations for carbon dioxide and carbonic acid， and the transition states， which are the states mediated by water molecules have been vigorously performed， however， there are limitations in terms of computational speed and resources for exploring various conformations and reaction mechanisms in the water molecule network through ab initio calculations. The machine learning potential (MLP) was created by using the DeepMD framework， which is one of the MLPs that have been reported to be applied in various systems. We analyzed the conformation of the dissociation process of carbonic acid by using the Nudged Elastic Band method in the molecular dynamics (MD) calculations. The energy difference between MLP-MD and ab-initio MD (AIMD) at the beginning and the end of the reaction was 0.109 kcal/mol/atoms. Looking at the trend of the barrier value with respect to the number of water molecules in the water molecular network， the barrier value to decrease with the increase in the number of water molecules has similar tendency of the previous quantum chemical and AIMD calculations.