Kim Jelfs

2022 United Kingdom Award Winner — Faculty

Current Position:
Reader of Computational Materials Discovery & Royal Society University Research Fellow

Institution:
Imperial College London

Discipline:
Theoretical Chemistry

Recognized for:  The development of revolutionary computer software that enables the accelerated discovery of new supramolecular materials, greatly expediting the process of developing new materials for applications from photonics to energy storage.

Areas of Research Interest and Expertise: Materials Chemistry; Supramolecular and Macromolecular Chemistry; Open-source Software; Artificial Intelligence; Computational Materials Discovery

Previous Positions: 

MSci in Chemistry, University College London, UK
PhD in Chemistry, University College London, UK
Postdoctoral Researcher, University of Liverpool, UK

Research Summary: Most materials are made up of molecules and atoms that assemble in an orderly fashion. Materials that are composed of discrete molecular entities held together by weak, intramolecular bonds between molecules are often referred to as supramolecular assemblies. Though these assemblies can have unique properties and functions, their characteristics are difficult to predict without actually synthesizing them in a laboratory—a process that is time-consuming and often gives unpredictable results.

Kim Jelfs, PhD, and her group have developed revolutionary, open-source software capable of predicting the properties and functions of advanced supramolecular materials. A development that has led to collaborative work with synthetic laboratories all over the world and subsequently the creation of new molecules and materials that would not have been possible without her predictive software.

Supramolecular assemblies and the materials they constitute have found uses in a variety of areas including sensing, photonic devices, catalysis, and energy storage. Traditionally, these materials are developed through trial and error because supramolecular assemblies do not always come together in a predictable or orderly manner. Jelfs' software contains an algorithm that can predict not only how the molecules will assemble, but also the subsequent chemical and physical properties of the resulting materials. Jelfs has also incorporated artificial intelligence and deep learning methods that enable the prediction of novel systems. Close collaboration with synthetic chemists has already produced new materials with exciting properties, and future adaptation of the software will expand the scope beyond supramolecular assemblies into other systems and applications. 

“I am so happy for this recognition of my group's work! It's a privilege to work with so many talented people to develop computational approaches aimed at tackling challenges in materials discovery, inspired by our fantastic synthetic collaborators.”

Key Publications: 

1. Turcani, E. Berardo, K.E. Jelfs. stk: A Python Toolkit for Supramolecular Assembly. J. Computational Chem., 2018.
2. Berardo, L. Turcani, M. Miklitz, K.E. Jelfs. An Evolutionary Algorithm for the Discovery of Porous Organic Cages. Chem. Sci. 2018.
3. R.L. Greenaway, V. Santolini, M.J. Bennison, B.M. Alston, C.J. Pugh, M.A. Little, M. Miklitz, E.G.B. Eden-Rump, R. Clowes, A. Shakil, H.J. Cuthbertson, H. Armstrong, M.E. Briggs, K.E. Jelfs, A.I. Cooper. High-throughput Discovery of Organic Cages and Catenanes using Computational Screening Fused with Robotic Synthesis. Nat. Commun. 2018.
4. 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. From Concept to Crystals via Prediction: Multi-component Organic Cage Pots by Social Self-sorting. Angew. Chem. 2019.

Other Honors: 

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