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.
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.
Organic structure
directing agents (OSDAs) play a crucial role
in the synthesis of micro- and mesoporous materials especially in
the case of zeolites. Despite the wide use of OSDAs, their interaction
with zeolite frameworks is poorly understood, with researchers relying
on synthesis heuristics or computationally expensive techniques to
predict whether an organic molecule can act as an OSDA for a certain
zeolite. In this paper, we undertake a data-driven approach to unearth
generalized OSDA–zeolite relationships using a comprehensive
database comprising of 5,663 synthesis routes for porous materials.
To generate this comprehensive database, we use natural language processing
and text mining techniques to extract OSDAs, zeolite phases, and gel
chemistry from the scientific literature published between 1966 and
2020. Through structural featurization of the OSDAs using weighted
holistic invariant molecular (WHIM) descriptors, we relate OSDAs described
in the literature to different types of cage-based, small-pore zeolites.
Lastly, we adapt a generative neural network capable of suggesting
new molecules as potential OSDAs for a given zeolite structure and
gel chemistry. We apply this model to CHA and SFW zeolites generating
several alternative OSDA candidates to those currently used in practice.
These molecules are further vetted with molecular mechanics simulations
to show the model generates physically meaningful predictions. Our
model can automatically explore the OSDA space, reducing the amount
of simulation or experimentation needed to find new OSDA candidates.
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