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Machine learning for organic cage property prediction

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Title: Machine learning for organic cage property prediction
Authors: Turcani, L
Greenaway, RL
Jelfs, KE
Item Type: Journal Article
Abstract: We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt.
Issue Date: 12-Feb-2019
Date of Acceptance: 1-Dec-2018
URI: http://hdl.handle.net/10044/1/65046
DOI: https://dx.doi.org/10.1021/acs.chemmater.8b03572
ISSN: 0897-4756
Publisher: American Chemical Society (ACS)
Start Page: 714
End Page: 727
Journal / Book Title: Chemistry of Materials
Volume: 31
Issue: 3
Copyright Statement: © 2018 American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemistry of Materials, after peer review and technical editing by the publisher. To access the final edited and published work see https://dx.doi.org/10.1021/acs.chemmater.8b03572
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/M017257/1
EP/P005543/1
758370
Keywords: Science & Technology
Physical Sciences
Technology
Chemistry, Physical
Materials Science, Multidisciplinary
Chemistry
Materials Science
NEURAL-NETWORKS
FORCE-FIELD
MOLECULES
CHEMISTRY
DESIGN
Materials
03 Chemical Sciences
09 Engineering
Publication Status: Published
Article Number: acs.chemmater.8b03572
Online Publication Date: 2018-12-10
Appears in Collections:Chemistry
Faculty of Natural Sciences