Unraveling Oxidation Behaviors of MXenes in Aqueous Systems by Active Learning Potential Molecular Dynamics Simulation.

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Abstract

MXenes are two-dimensional (2D) materials with great potential in application to various fields. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but the low cost. The oxidation behaviors of the super large MXenes aqueous system are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. And free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponential decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation reaction in the super-sized MXene aqueous system. This significant breakthrough opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes. Besides, the developed NNP could be used in other applications of complex aqueous MXene systems after adding new data.© 2023 Wiley-VCH GmbH.