Imperial College London


Faculty of Natural SciencesDepartment of Chemistry

Reader in Computational Materials Chemistry



+44 (0)20 7594 3438k.jelfs Website




207AMolecular Sciences Research HubWhite City Campus





You can find our group website with much more information at:

We use computational approaches towards enabling functional molecular material discovery. Specifically, we investigate predicting these materials’ assembly as individual units and how this then affects self-assembly and properties. We aim to apply this to large scale computational screening of precursor libraries, creating databases of viable, functional materials. Our strong ongoing links with synthetic collaborators allow synthetic realisation of the predictions. We focus on porous molecular materials, organic electronics and membrane materials. We are currently funded by the Royal Society, the Engineering and Physical Sciences Research Council (EPSRC), the Leverhulme Trust and the European Research Council (ERC), as well as industry.Cage molecules

Selected Publications

Journal Articles

Thompson KA, Mathias R, Kim D, et al., 2020, N-Aryl-linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures, Science, Vol:369, ISSN:0036-8075, Pages:310-315

Yuan Q, Santana Bonilla A, Zwijnenburg MA, et al., 2020, Molecular generation targeting desired electronic properties via deep generative models, Nanoscale, Vol:12, ISSN:2040-3364, Pages:6744-6758

Tan R, Wang A, Malpass-Evans R, et al., 2020, Hydrophilic microporous membranes for selective ion separation and flow-battery energy storage, Nature Materials, Vol:19, ISSN:1476-1122, Pages:195-202

Miklitz M, Turcani L, Greenaway RL, et al., 2020, Computational discovery of molecular C60 encapsulants with an evolutionary algorithm, Communications Chemistry, Vol:3, ISSN:2399-3669

Jelfs K, Greenaway RL, Santolini V, et al., 2019, From concept to crystals via prediction: multi‐component organic cage pots by social self‐sorting, Angewandte Chemie - International Edition, Vol:131, ISSN:1433-7851, Pages:16421-16427

Turcani L, Greenaway RL, Jelfs KE, 2019, Machine learning for organic cage property prediction, Chemistry of Materials, Vol:31, ISSN:0897-4756, Pages:714-727

Miklitz M, Jelfs K, 2018, pywindow: automated structural analysis of molecular pores, Journal of Chemical Information and Modeling, Vol:58, ISSN:1549-9596, Pages:2387-2391

Berardo E, Miklitz M, Turcani L, et al., 2018, An evolutionary algorithm for the discovery of porous organic cages, Chemical Science, Vol:9, ISSN:2041-6520, Pages:8513-8527

Turcani L, Berardo E, Jelfs KE, 2018, stk : A Python toolkit for supramolecular assembly, Journal of Computational Chemistry, Vol:39, ISSN:0192-8651, Pages:1931-1942

Greenaway R, Santolini V, Bennison MJ, et al., 2018, High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis, Nature Communications, Vol:9, ISSN:2041-1723

Rice B, LeBlanc LM, Otero-de-la-Roza A, et al., 2018, A computational exploration of the crystal energy and charge-carrier mobility landscapes of the chiral [6]helicene molecule, Nanoscale, Vol:10, ISSN:2040-3364, Pages:1865-1876

More Publications