A priori control of zeolite phase competition and intergrowth with high-throughput simulations

Abstract

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. Here, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from over 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalize framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed realizing an intergrowth zeolite through a single bi-selective template. The computation-first approach enabled controlling both zeolite synthesis and structure composition using a priori theoretical descriptors.

Machine learning potential era of zeolite simulation

“…Beyond the approach in minimizing Δ E zeo-SDAs , Schwalbe-Koda et al 16 proposed a phase competition mechanism; that is, an optimal SDA must exhibit both strong binding affinity toward the desired zeolite and weak binding affinity toward all other frameworks. By computing the binding energy for each SDA-zeolite pair, they obtained a binding matrix with ∼112 400 entries ( Fig.…”

Advances in the synthesis and application of the SSZ-39 zeolite

“…In addition, these obtained SSZ-39 zeolite products showed comparable crystallinity and textural properties to the conventional one. 31 Feng-Shou Xiao…”

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

“…With that in mind, the shape of the different OSDAs was quantified by projecting the three‐dimensional molecular conformer into a two‐dimensional plane, followed by measuring its major axes in the projection (SI for details). Then, the binding energies of the OSDAs are averaged over the shape domain, giving rise to Figure 1c [20] . OSDA3 is aligned between OSDA1 and OSDA2, molecules that preferably stabilizes cha and aei cages, respectively.…”

“…This complexity can be quantified using cheminformatics or through expert inspection. [20] To obtain detailed information of an intergrown material, it is mandatory to use characterization techniques that can adequately probe local intracrystalline structural features. Although transmission electron microscopy (TEM) can interrogate the local structure of zeolites, these materials are sensitive to the electron beam dose, requiring strict management of electron beam illumination to avoid damage.…”

“…Then, the binding energies of the OSDAs are averaged over the shape domain, giving rise to Figure 1c. [20] OSDA3 is aligned between OSDA1 and OSDA2, molecules that preferably stabilizes cha and aei cages, respectively. The shape of OSDA3 also lies in a region of biselectivity towards CHA/AEI, as demonstrated by the transition between CHA (red) and AEI (blue) selectivity domains in the shape space in Figure 1c.…”