Necessity of Heteroatoms for Realizing Hypothetical Aluminophosphate Zeolites: A High-Throughput Computational Approach

Abstract

Aluminophosphate zeolites, including pure aluminophosphate (AlPO) and heteroatom-stabilized AlPO zeolites, have important applications in adsorption, separation and heterogeneous catalysis. Thus far, millions of hypothetical zeolite structures have been predicted, providing a large number of candidates to be synthetically targeted. However, their realization in experiment still requires a priori knowledge on whether heteroatoms are necessary in the synthetic preparation in order to stabilize a specific zeolite topology. To this end, many computational efforts have been made to compare the differences in framework energies and distortions before and after heteroatom incorporation. However, such an approach is not generally applicable for high-throughput computations, because of the combinatorial explosion of potential heteroatom incorporation sites in a hypothetical zeolite framework. Here, we establish a quantitative model to estimate the probability of a hypothetical framework being realizable as a pure AlPO or a heteroatom-stabilized AlPO zeolite. This model is based on Mahalanobis distances between hypothetical structures and their neighboring reference structures in distortion-energy plots. Our approach only requires onestep geometry optimization on zeolite frameworks as pure AlPO polymorphs without building many heteroatom-containing models, ensuring its applications in high-throughput structure evaluation and screening. We employed this approach on 84,292 hypothetical ABC-6 zeolite structures, and discovered that 17,050 of them could be realizable as pure AlPOs and 12,039 only realizable via heteroatom incorporation. Our results will provide important guidance toward the synthesis of new aluminophosphate zeolites.

Zeolite Adsorption Isotherms Predicted by Pore Channel and Local Environmental Descriptors: Feature Learning on DFT Binding Strength

“…35 With an emphasis laid on the T atom density, the number of T atoms in unit cell and the framework density (FD), which is the number of T atoms per 1000 Å 3 , are also very useful parameters to account for the thermodynamic stability and feasibility of zeolites. 3,36,37 Although most of the known zeolites have the medium-or large-sized pores (<18-ring) with the FD value being no less than 12, some theoretically predicted extra-largepore zeolites with low FD (10.9) have been synthesized. 36 Some other widely used parameters could be derived from the structural information by using various programs such as Zeo+ +, FraGen, ZEOMICS, and so on.…”

“…45 The distributions of Si−O bond lengths and Si−O−Si angles have also been used to correlate with the mechanical and feasibility properties of zeolite frameworks. 33,34,37 For prediction of the feasibility of zeolite frameworks, there are also some energy-based features, such as relative energy, relative lattice enthalpies, and relative formation energy with respect to quartz or berlinite. 34,37 In the following part, we will show that the binding energies of guest molecules have a close relationship with the energy-based parameters.…”

Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat

“…Restricting the range of correlations or discarding all angular information leads to degradation of classification performance, indicating that the structural features that distinguish real and hypothetical zeolites involve angular correlations and patterns in the relative positions of second and third neighbor atoms -i.e., at length scales beyond the typical indicators that have been hypothesized in previous studies [11][12][13][14][15][16][17][18].…”

“…Which hypothetical zeolites are most likely synthesizable, and in which chemical composition? Previous attempts to answer these questions [11][12][13][14][15][16][17][18] have relied on intuitive guesses for structural descriptors such as rings and angles, which provide incomplete [19] and thus biased results. In the present work, we answer all these questions via rigorous data science methods combining unsupervised and supervised machine learning, [20] along with the generalized convex hull (GCH) description of thermodynamic stability, [21] yielding a new and powerful approach for sorting real [22] and hypothetical [2][3][4][5] zeolites, as well as finding promising zeolite candidates and suggesting likely chemical compositions for them.…”

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

“…Innovative work by Li et al generated over 80 000 hypothetical AlPO structures by variation of the stacking sequence of six rings, resulting in two newly synthesized materials; [ 206 ] this work led on to further high throughput studies by the authors, examining the necessity for heteroatoms in their database of structures. [ 207 ] Although useful, this database is far less extensive than the Deem database owing to its method of construction, as it does not explore the varied composite building units that zeolites can be made from.…”