糖心TV

Computational discovery of molecular materials

We have been developing computational software towards assisting in the discovery of molecular materials with targeted structures and properties. 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 semiconductors or for photocatalysis. Intrinsically porous organic molecules have shown promise in separations, catalysis, encapsulation, sensing, and as porous liquids. These molecules are typically synthesised from organic precursors through dynamic covalent chemistry (DCC). If we consider cages synthesised from imine condensation reactions alone, there are approximately 800,000 possible aldehyde and amine precursors, combining these in all the different possible topologies results in over 830 million possible porous organic cages. Therefore, either from a computational or synthetic perspective, it is not possible for us to screen all these possible assemblies. Our evolutionary algorithm automates the assembly of hypothetical molecules from a library of precursors. The software belongs to the class of approaches inspired by Darwin's theory of evolution and the premise of "survival of the fittest". Our approach has already suggested promising targets that have been synthetically realised. Further, we are addressing questions such as which topologies or DCC reactions maximise void size or whether specific chemical functionalities promote targeted applications. We have also examined the application of machine learning for the rapid prediction of whether porous organic molecules will be shape persistent, retaining an internal cavity, or not. We have further trained a model (the Materials Precursor Score, MPScore) to guide our predictions to select materials that have a high chance of being synthesisable in the laboratory. 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 membranes for molecular separations and in energy storage devices.

References:

鈥淓xplainable Graph Neural Networks for Organic Cages鈥�, Q. Yuan, F. Szczypi艅ski, K. E. Jelfs*, Digital Discovery (2022), DOI: 10.1039/D1DD00039J

鈥淢aterials Precursor Score: Modelling Chemists鈥� Intuition for the Synthetic Accessibility of Porous Organic Cages鈥�, S. Bennett, F. T. Szczypi艅ski, L. Turcani, M. E. Briggs, R. L. Greenaway, K. E. Jelfs, J. Chem. Inf. Model. (2021), 61, 9, 4342–4356

鈥淗igh-throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design鈥�, A. Tarzia, J. Lewis,* K. E. Jelfs*, Angew. Chem. Int. Ed. (2021), 60, 20879–20887

鈥淪terics and Hydrogen Bonding Control Stereochemistry and SelfSorting in BINOL-Based Assemblies鈥�, Y.-Q. Zou, D. Zhang, T. K. Ronson, A. Tarzia, Z. Lu, K. E. Jelfs,* J. R. Nitschke*, J. Am. Chem. Soc. (2021), 143, 24, 9009-9015.

鈥淐an we predict materials that can be synthesised?鈥� (Review), F. T. Szczypi艅ski, S. Bennett and K. E. Jelfs, Chem. Sci. (2021), 12, 830-840.

鈥淣-Aryl–linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures鈥�, K. A. Thompson, R. Mathias, D. Kim, J. Kim, N. Rangnekar, 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 (2020), 369 (6501), 310-315.

鈥淐omputational Discovery of Molecular C60 Encapsulants with an Evolutionary Algorithm鈥�, M. Miklitz, L. Turcani, R. L. Greenaway, K. E. Jelfs, Commun. Chem. (2020), 3 (10).

鈥淗ydrophilic microporous membranes for selective ion separation and flow-battery energy storage鈥�, 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 (2020), 19, 195-202.

鈥淔rom Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting鈥�, 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 (45) 16421-16427.

"Machine Learning for Organic Cage Property Prediction", L. Turcani, R. L. Greenaway, K. E. Jelfs, Chem. Mater. (2019) 31, 3, 714-727.

"An Evolutionary Algorithm for the Discovery of Porous Organic Cages", E. Berardo, L. Turcani, M. Miklitz, K. E. Jelfs, Chem. Sci. (2018), 9, 8513.