Automated flow synthesis of algorithmically designed ferroelectric nematogens
The synthesis of the ferroelectric nematic material RM734 is telescoped into a single continuous flow process, utilising inline liquid-liquid separation to remove reactants and by-products from the reaction stream. Following the final synthetic step, we subject the semi-crude material to offline aut...
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Published in | Liquid crystals Vol. 50; no. 3; pp. 534 - 542 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis
19.02.2023
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
ISSN | 0267-8292 1366-5855 |
DOI | 10.1080/02678292.2023.2208550 |
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Summary: | The synthesis of the ferroelectric nematic material RM734 is telescoped into a single continuous flow process, utilising inline liquid-liquid separation to remove reactants and by-products from the reaction stream. Following the final synthetic step, we subject the semi-crude material to offline automated chromatography. Materials can be prepared on a gram scale (12 minutes) and chromatographically purified (8 minutes) in around 20 minutes. Over a given time interval the system described herein can prepare a far larger number of materials (on gram scale) than an experienced chemist. Secondly, we implement an established process for generation of new molecular structures via a genetic algorithm, which generates many new candidate structures per iteration. A filter removes undesirable structures, and a fast semi-empirical QM calculation is performed on those that remain. A neural network trained against a library of nematic and ferroelectric nematic materials is used to rank the candidate structures based on the QM output and a fingerprinting process, with the highest scoring candidates then used to seed a new round of mutations. We combine these two approaches; we generated and ranked several tens of thousands of variants of RM734 and synthesised a small library of materials using our continuous flow protocol. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0267-8292 1366-5855 |
DOI: | 10.1080/02678292.2023.2208550 |