Germanium distributions in zeolites derived from neural network potentials

Abstract

Germanosilicate zeolites have played a pivotal role in the recent surge in synthesis of novel zeolite topologies. This success has been attributed to the combined effect of the high hydrolytic lability and specific distribution of germanium within the zeolitic framework, e.g., favoring the double four-ring (D4R) structural units. While experimental determination of germanium distributions remains limited, their in silico investigation has been hampered by the high computational cost of ab initio calculations. To overcome these limitations, we have developed neural network potentials (NNPs) that can efficiently explore a wide range of distributions in germanosilicate zeolites, while maintaining the accuracy of the ab initio (dispersion-corrected GGA DFT) training dataset. Through comprehensive screening of low-energy germanium distributions for five zeolite topologies (UTL, BEC, UOV, IWW and *CTH) across a broad range of Si/Ge ratios, we have identified a key factor governing the distribution of germanium in these D4R-containing zeolites, which is the tendency of germanium to cluster. The clustering initiates at the D4R units, leading to a preference to occupy these units at low to medium Ge loading (Si/Ge ≥ 5). However, at high Ge loading, the Ge tends to phase separate into Ge-rich and Ge-poor regions, regardless of the specific structural unit. The zeolite topology was shown to be capable of modulating these trends in germanium distribution (e.g., UTL strongly favors D4R occupation even for low Si/Ge ratios), which suggests the possibility to develop design strategies for targeted zeolite synthesis. The NNPs presented herein enable rapid evaluation of these design strategies across a wide range of candidate zeolite structures and experimentally relevant Si/Ge ratios.