UK Chemistry Group Develops Software In Hunt For New Materials

Kim Jelfs, a computational chemist at Imperial College London (ICL), has developed software that predicts thousands of new materials each day and analyzes which ones are good and can be made in the lab, or they end as completely meaningless for research, Jennifer Newton reported for Chemistry World.

Photo Insert: Computational chemist at Imperial College London (ICL), Kim Jelfs

Her group is comprised of computational chemists who do not work in isolation and are actively embedded in experimental studies. Jelfs’ ultimate aim is to help her experimental colleagues find new materials quicker.

The group has a key piece of software written in Python called the supramolecular toolkit (stk). It was first developed by a talented student, but everyone in the group now adds to it. They also augment other software – particularly open-source software – made by other groups by, for example, coding workflows to make processes automatic for high throughput systems.

The team first developed their supramolecular toolkit to screen porous molecules. It starts with organic building blocks, or precursors, and assembles them into different topologies. It then looks for the low energy structures, which are the ones that you would expect to observe experimentally, before the next part of the software analyses the materials’ properties. Porous organic molecules have potential applications as membranes, and in sensing and catalysis.

“Because porous molecular materials are modular, they have solution-processability benefits,” explains Jelfs. “And computationally, being modular gives us the opportunity to screen them in a completely different way than you would be able to for zeolites and MOFs. You can potentially combine different modules in one material to allow really fine control of their properties.”

The team can also apply their supramolecular toolkit to other systems that have organic building blocks. For example, they’ve recently worked with a collaborator to screen 350,000 different linear polymers for optoelectronic properties. They’ve also extended their software to look for metal–organic cages.

Being able to adapt their software and processes to new systems is a priority for Jelfs.

“We’re now getting approached by different experimental groups, asking us to look at other classes of materials, and it’s relatively simple to add that functionality onto our software. What takes a long time is having in-depth knowledge as to what are the problems for a particular class of materials, as well as practicalities from our computational side. So how to get a good description and how to pick out the key features of those systems that are relevant for our collaborators.”

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