Computational modeling to assist in the discovery of supramolecular materials

Abstract

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted.A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Supramolecular Coordination Cages for Artificial Photosynthesis and Synthetic Photocatalysis

“…Additionally, computational work toward the prediction of self-assembly, binding, and catalysis within coordination cages has recently gained attention. 70,71 Small molecules (such as substrates, intermediates, and catalysts) can be selectively bound within the cavity. 72,73 Supramolecular coordination cages have been extensively researched in various kinds of chemical transformations in which their porosity is exploited to preorganize substrates in the accessible cavity.…”

“…The chemical environment in these cavities can be tuned by the nature of ligands, (endohedral) functional groups attached to the ligands, as well as the charge that is induced by the metal connecting nodes. Additionally, computational work toward the prediction of self-assembly, binding, and catalysis within coordination cages has recently gained attention. , Small molecules (such as substrates, intermediates, and catalysts) can be selectively bound within the cavity. , …”

Design and assembly of porous organic cages

“…To expedite this, the concept of ‘inverse design’ has emerged. 87,88 Rather than starting with material synthesis, ‘inverse design’ begins with a specific application in mind. It facilitates the computational design of materials precisely crafted for that intent.…”

Deep generative design of porous organic cages via a variational autoencoder

“…Porous organic cages (POCs) are a class of molecular materials featuring an intrinsic cavity that can be accessed by several windows that allow bidirectional molecular passage. 1–3 Compared to other porous materials, this intrinsic cavity is enclosed by the molecule itself so that the cavity is observable even in the form of a single discrete molecule. Due to the intrinsic void space in the solid state and the discrete form, POCs have potential in various applications such as molecular separations, 4,5 sensing, 6 proton conduction 7 and catalysis.…”