Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential.

12 July 2023, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first principles) computational efforts to understand the mechanistic underpinning of the water-zeolite interactions, it is still prohibitively expensive to carry out comprehensive studies employing realistic zeolitic models. Therefore, we used a recently developed reactive neural network-based potential for aluminosilicate zeolites in the protonic form including their interaction with the aqueous solution that has a capacity to accelerate simulation by orders of magnitude while retaining the reference level of accuracy. We used it to determine how multiple factors (aluminum content, water loading and temperature) influence the proton solvation and water dynamics in one of the industrially most important acidic zeolites, the faujasite (FAU). We found that Si/Al ratio is a significant determinant of the water diffusivity, water capacity to solvate protons in the nanopores, and the zeolite hydrolytic stability. We expect that many of these findings are readily extendable to other acidic zeolites in interaction with water.


machine learning potentials
neural networks
acid zeolites
proton solvation


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