Premium
From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting
Author(s) -
Greenaway Rebecca L.,
Santolini Valentina,
Pulido Angeles,
Little Marc A.,
Alston Ben M.,
Briggs Michael E.,
Day Graeme M.,
Cooper Andrew I.,
Jelfs Kim E.
Publication year - 2019
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201909237
Subject(s) - component (thermodynamics) , amine gas treating , crystal (programming language) , chemistry , sorting , aldehyde , molecule , cage , self assembly , crystal structure , crystallography , stereochemistry , chemical physics , organic chemistry , computer science , combinatorics , mathematics , algorithm , physics , catalysis , thermodynamics , programming language
We describe the a priori computational prediction and realization of multi‐component cage pots, starting with molecular predictions based on candidate precursors through to crystal structure prediction and synthesis using robotic screening. The molecules were formed by the social self‐sorting of a tri‐topic aldehyde with both a tri‐topic amine and di‐topic amine, without using orthogonal reactivity or precursors of the same topicity. Crystal structure prediction suggested a rich polymorphic landscape, where there was an overall preference for chiral recognition to form heterochiral rather than homochiral packings, with heterochiral pairs being more likely to pack window‐to‐window to form two‐component capsules. These crystal packing preferences were then observed in experimental crystal structures.