Computational screening for nested organic cage complexes

Abstract

Computational simulations were used to screen 8712 combinations of porous organic cages for energetically favourable nested cage complexes.

Enabling Technology for Supramolecular Chemistry

“…It should also be noted, that whilst this review has focussed on the benefits of enabling technology, it is now frequently coupled with computation to carry out prior predictions and subsequent analysis. Growing examples can already be seen of computational predictions in areas such as organic cages ( Berardo et al, 2020 ), photocatalysts ( Singh et al, 2015 ), and drug molecules ( Yu et al, 2020 ). The likely outlook for the future of chemistry, including supramolecular chemistry, is a hybrid approach of automated experimentation coupled with computation, whether that is using computation to first narrow down the design space, or using HTS to collect large amounts of robust data to feed into data-led computational approaches and machine learning algorithms.…”

Recent progress and future challenges in the supramolecular polymerization of metal-containing monomers

“…107 In other fields, such as crystal structure prediction or porous materials design, this prospect has already become reality and we expect a similar development for supramolecular polymerization. [108][109][110][111] Particularly, aspects of molecular design strategies that can be difficult to estimate beforehand, such as the formation of macrodipoles, the accumulation or delocalization of charge and synergistic effects upon self-assembly, will become more accessible with the combination of synthetic work and simulations. This dual approach requires a more intimate collaboration between research groups working on molecular self-assembly in the lab and in silico.…”

High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages

“…In the past few years, there has been emerging interest in the development of high-throughput computation-only [12] and synthesis-only [13] workflows targeting the discovery of materials with particular applications. For example, these methods have been successfully applied to organic, [14][15][16][17][18][19] hybrid, [20][21][22][23] and inorganic materials, [24] ranging from discrete molecules [17,18] to polymers, [14,15] frameworks, [20][21][22][23] and liquids, [16] and includes targeted screening of their solubility, [16] mechanical properties, [14] porosity, [20,[22][23][24] optical band gaps, [19] and their potential to form host-guest complexes. [17] However, while there is this handful of studies on the high-throughput synthesis of materials such as metal-organic frameworks, zeolites, and organic polymers, the use of robotic synthesis for supramolecular self-sorted organic materials and assemblies is rare, which is partly due to the difficulties in predicting the product outcome and the difficulty raised in characterising libraries of supramolecules.…”

“…The automated assembly of systems allows for 100,000s of possibilities to be tested, for example as we have conducted for identifying promising nested-cage systems. [17] The latter is an example of a brute-force screening approach, where all possibilities are enumerated and tested. However, the size of the search space for organic cage precursors is enormous, so it is not possible to test every combination.…”