From isomorphism to polymorphism: connecting interzeolite transformations to structural and graph similarity

Zeolites are nanoporous crystalline materials with abundant industrial applications. Despite sustained research, only 235 different zeolite frameworks have been realized out of millions of hypothetical ones predicted by computational enumeration. Structure-property relationships in zeolite synthesis are very complex and only marginally understood. Here, we apply structure and graph-based unsupervised machine learning to gain insight on zeolite frameworks and how they relate to experimentally observed polymorphism and phase transformations. We begin by describing zeolite structures using the Smooth Overlap of Atomic Positions method, which clusters crystals with similar cages and density in a way consistent with traditional hand-selected composite building units. To also account for topological differences, zeolite crystals are represented as multigraphs and compared by isomorphism tests. We find that fourteen different pairs and one trio of known frameworks are graph isomorphic. Based on experimental interzeolite conversions and occurrence of competing phases, we propose that the availability of kinetic-controlled transformations between metastable zeolite frameworks is related to their similarity in the graph space. When this description is applied to enumerated structures, over 3,400 hypothetical structures are found to be isomorphic to known frameworks, and thus might be realized from their experimental counterparts. Using a continuous similarity metric, the space of known zeolites shows additional overlaps with experimentally observed phase transformations. Hence, graph-based similarity approaches suggest a venue for realizing novel zeolites from existing ones by providing a relationship between pairwise structure similarity and experimental transformations.Comment: 11 pages, 6 figure

Convolutional Networks on Graphs for Learning Molecular Fingerprints.

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.Chemistry and Chemical Biolog

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

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this work, 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 more than 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalized 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 us to realize an intergrowth zeolite through a single bi-selective template. The computation-first approach enables control of both zeolite synthesis and structure composition using a priori theoretical descriptors.D.S.-K. and R.G.-B. acknowledge the Energy Initiative (MITEI) and MIT International Science and Technology Initiatives (MISTI) Seed Funds. D.S.-K. was also funded by the MIT Energy Fellowship. C.P., E.B.-J., M.M., and A.C. acknowledge financial support by the Spanish government through the “Severo Ochoa” program (SEV-2016-0683, MINECO) and grant RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE). E.B.-J. acknowledges the Spanish government for an FPI scholarship (PRE2019-088360). Z.J., E.O., S.K., and Y.R.-L. acknowledge partial funding from Designing Materials to Revolutionize and Engineer our Future (DMREF) from the National Science Foundation (NSF); awards 1922311, 1922372, and 1922090; and the Office of Naval Research (ONR) under contract N00014-20-1-2280. S.K. was additionally funded by the Kwanjeong Educational Fellowship. Z.J. was also supported by the Department of Defense (DoD) through the National Defense Science Engineering Graduate (NDSEG) fellowship program. T.W. acknowledges financial support by the Swedish Research Council (grant no. 2019-05465). Computer calculations were executed at the Massachusetts Green High-Performance Computing Center with support from MIT Research Computing and at the Extreme Science and Engineering Discovery Environment (XSEDE) (53) Expanse through allocation TG-DMR200068

The Materials Research Platform: Defining the Requirements from User Stories

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform

Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites

Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding towards a given zeolite framework. Over the last decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that binding energies from the frozen pose method correlate best with DFT time-averaged energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over single-point calculations. The code and benchmark data are open-sourced and provide robust and computationally-efficient guidelines to calculating binding energies in zeolite-OSDA pairs

Supramolecular Recognition in Crystalline Nanocavities Through Monte Carlo and Voronoi Network Algorithms

Computational screening of templating molecules enables the discovery of new synthesis routes for zeolites. Despite decades of work in molecular modeling of organic structure-directing agents (OSDAs), the development and benchmarking of algorithms for docking molecules in nanoporous materials has received scarce attention. Here, we introduce Voronoi Organic-Inorganic Docker (VOID) a method based on Voronoi diagrams to dock molecules in crystalline materials, and release it as a Python package. Benchmarks of the implementation show it generates docked poses up to 95 times faster than the traditional Monte Carlo docking scheme. We then evaluate the algorithm by obtaining binding energies for about 120 zeolite-OSDA pairs of industrial relevance. The computed host-guest interactions explain experimental outcomes for traditional synthesis routes from the literature. The results further suggest new OSDAs to synthesize known zeolites. Finally, we exemplify the generality of VOID by docking molecules inside a metal-organic framework and on a metal surface. The proposed method and software provide a low-cost computational approach for generating molecule-material interfaces