Imperial College London
Portrait Picture

Dr Becky Greenaway

Faculty of Natural Sciences, Department of Chemistry

Senior Lecturer
 
 
 
//

Contact

 

r.greenaway Website

 
 
//

Location

 

401CMolecular Sciences Research HubWhite City Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Bennett:2021:10.1021/acs.jcim.1c00375,
author = {Bennett, S and Szczypiski, F and Turcani, L and Briggs, M and Greenaway, RL and Jelfs, K},
doi = {10.1021/acs.jcim.1c00375},
journal = {Journal of Chemical Information and Modeling},
pages = {4342--4356},
title = {Materials precursor score: modelling chemists' intuition for the synthetic accessibility of porous organic cage precursors},
url = {http://dx.doi.org/10.1021/acs.jcim.1c00375},
volume = {61},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties.
AU  - Bennett,S
AU  - Szczypiski,F
AU  - Turcani,L
AU  - Briggs,M
AU  - Greenaway,RL
AU  - Jelfs,K
DO  - 10.1021/acs.jcim.1c00375
EP  - 4356
PY  - 2021///
SN  - 1549-9596
SP  - 4342
TI  - Materials precursor score: modelling chemists' intuition for the synthetic accessibility of porous organic cage precursors
T2  - Journal of Chemical Information and Modeling
UR  - http://dx.doi.org/10.1021/acs.jcim.1c00375
UR  - https://pubs.acs.org/doi/full/10.1021/acs.jcim.1c00375
UR  - http://hdl.handle.net/10044/1/90526
VL  - 61
ER  -
Download