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SUMMARY:Computational discovery of porous molecular materials - Kim Jelfs\
 , Imperial
DTSTART:20220321T140000Z
DTEND:20220321T143000Z
UID:TALK167306@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:We have been developing computational software towards assisti
 ng in the discovery of molecular materials with targeted structures and pr
 operties. Whilst initially we have focused upon porous molecular materials
 \, we will also address the ways in which our approach is generalisable to
  other molecular materials and their applications\, including as organic s
 emiconductors or for photocatalysis. Intrinsically porous organic molecule
 s have shown promise in separations\, catalysis\, encapsulation\, sensing\
 , and as porous liquids. These molecules are typically synthesised from or
 ganic precursors through dynamic covalent chemistry (DCC). If we consider 
 cages synthesised from imine condensation reactions alone\, there are appr
 oximately 800\,000 possible aldehyde and amine precursors\, combining thes
 e in all the different possible topologies results in over 830 million pos
 sible porous organic cages. Therefore\, either from a computational or syn
 thetic perspective\, it is not possible for us to screen all these possibl
 e assemblies. Our evolutionary algorithm automates the assembly of hypoth
 etical molecules from a library of precursors. The software belongs to the
  class of approaches inspired by Darwin's theory of evolution and the prem
 ise of "survival of the fittest". Our approach has already suggested prom
 ising targets that have been synthetically realised. Further\, we are addr
 essing questions such as which topologies or DCC reactions maximise void s
 ize or whether specific chemical functionalities promote targeted applicat
 ions. We have also examined the application of machine learning for the ra
 pid prediction of whether porous organic molecules will be shape persisten
 t\, retaining an internal cavity\, or not. Finally\, we have trained a mod
 el (the Materials Precursor Score\, MPScore) to guide our predictions to s
 elect materials that have a high chance of being synthesisable in the labo
 ratory. More recently\, we have also extended our software and approach to
  the field of coordination cages. Finally\, I will discuss our work on the
  structure prediction of amorphous MOFs and porous organic polymer membran
 es for molecular separations and in energy storage devices.\n\n*References
 :*\n\n“Explainable Graph Neural Networks for Organic Cages”\, Q. Yuan\
 , F. Szczypiński\, K. E. Jelfs\, Digital Discovery (2022)\, DOI: 10.1039/
 D1DD00039J \n\n“Materials Precursor Score: Modelling Chemists’ Intuiti
 on for the Synthetic Accessibility of Porous Organic Cages”\, S. Bennett
 \, F. T. Szczypiński\, L. Turcani\, M. E. Briggs\, R. L. Greenaway\, K. E
 . Jelfs\, J. Chem. Inf. Model. (2021)\, 61\, 9\, 4342–4356 \n\n“High-t
 hroughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in S
 ystem Design”\, A. Tarzia\, J. Lewis\, K. E. Jelfs\, Angew. Chem. Int. E
 d. (2021)\, 60\, 20879–20887 \n\n“Sterics and Hydrogen Bonding Control
  Stereochemistry and SelfSorting in BINOL-Based Assemblies”\, Y.-Q. Zou\
 , D. Zhang\, T. K. Ronson\, A. Tarzia\, Z. Lu\, K. E. Jelfs\, J. R. Nitsch
 ke\, J. Am. Chem. Soc. (2021)\, 143\, 24\, 9009-9015.\n\n“Can we predict
  materials that can be synthesised?” (Review)\, F. T. Szczypiński\, S. 
 Bennett and K. E. Jelfs\, Chem. Sci. (2021)\, 12\, 830-840.\n\n“N-Aryl
 –linked spirocyclic polymers for membrane separations of complex hydroca
 rbon mixtures”\, K. A. Thompson\, R. Mathias\, D. Kim\, J. Kim\, N. Rang
 nekar\, J. R. Johnson\, S. J. Hoy\, I. Bechis\, A. Tarzia\, K. E. Jelfs\, 
 B. A. McCool\, A. G. Livingston\, R. P. Lively\, M. G. Finn\, Science (202
 0)\, 369 (6501)\, 310-315.\n\n“Computational Discovery of Molecular C60 
 Encapsulants with an Evolutionary Algorithm”\, M. Miklitz\, L. Turcani\,
  R. L. Greenaway\, K. E. Jelfs\, Commun. Chem. (2020)\, 3 (10).\n\n“Hydr
 ophilic microporous membranes for selective ion separation and flow-batter
 y energy storage”\, R. Tan\, A. Wang\, R. Malpass-Evans\, E. Wenbo Zhao\
 , T. Liu\, C. Ye\, X. Zhou\, B. Primera Darwich\, Z. Fan\, L. Turcani\, E.
  Jackson\, L. Chen\, S. Y. Chong\, T. Li\, K. E. Jelfs\, A. I. Cooper\, N.
  P. Brandon\, C. P. Grey\, N. B. McKeown\, Q. Song\, Nature Materials (202
 0)\, 19\, 195-202.\n\n“From Concept to Crystals via Prediction: Multi-Co
 mponent Organic Cage Pots by Social Self-Sorting”\, R. L. Greenaway\, V.
  Santolini\, A. Pulido\, M. A. Little\, B. M. Alston\, M. E. Briggs\, G. M
 . Day\, A. I. Cooper\, K. E. Jelfs\, Angew. Chem. Int. Ed. (2019)\, 131 (4
 5) 16421-16427.\n\n"Machine Learning for Organic Cage Property Prediction"
 \, L. Turcani\, R. L. Greenaway\, K. E. Jelfs\, Chem. Mater. (2019) 31\, 3
 \, 714-727.\n\n"An Evolutionary Algorithm for the Discovery of Porous Orga
 nic Cages"\, E. Berardo\, L. Turcani\, M. Miklitz\, K. E. Jelfs\, Chem. Sc
 i. (2018)\, 9\, 8513.
LOCATION:Venue to be confirmed
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