Professor Graeme Day joined the Chemical Science Editorial Board in 2021. To celebrate this occasion, we met virtually with Graeme to discuss his area of research and how he hopes to see his field progress in the next 10 years.
Graeme’s research focuses on the development of computational approaches for predicting the structures and properties of materials, focussing mainly on organic molecular crystals and their applications in a range of areas from pharmaceuticals to organic electronics. Graeme’s research group have also started to explore the use of machine learning methods for exploring chemical space to find new molecules with exceptional properties.
What excites you most about your area of research and what has been the most exciting moment of your career so far?
Being able to predict the structure of a new material computationally, with the structure then going on to be found experimentally, is very exciting. Going back about 20 years, this is not something that was thought to be possible! The idea that I have seen the crystal structure of a molecule, possibly before that molecule has ever been synthesised, is pretty cool. I feel lucky that we can collaborate and combine our computational work with the experimental work from other groups to uncover new materials. It’s exciting to think about all of the possibilities of these kinds of structure and property prediction methods.
With regards to the most exciting moment of my career, it is difficult to pinpoint one moment. However, in 2004, I was able to take part in my first blind crystal structure prediction test as an independent researcher. This was exciting! Being the only entrant to correctly predict one of the targets gave me a lot of confidence that I had valuable ideas and methods to bring to the field. The field has moved forward dramatically since 2004, but we still use some of the methods that I was working on back then.
What has been the most challenging moment of your career so far?
It’s probably the supervision of a research group that I have found the most challenging, but also very rewarding. Within a few years, I went from carrying out a lot of the research on my own, to receiving an ERC grant and being able to recruit a group of ten or so researchers. I had a very short period of time to learn how to make that transition and accept that I would spend more time discussing results, with less time doing hands-on research. Thankfully, I’ve had the opportunity to work with lots of great people and I hope that they have enjoyed their time in my research group.
Which of your Chemical Science publications are you most proud of and why?
That’s a tough one. Looking back, I’m quite proud of the range of work that we have published in Chemical Science, from fundamental questions about crystal packing, prediction of co-crystallisation, and machine learning applications for structure prediction. I really liked our 2014 contribution, which investigated the conformational preferences of molecules in their crystal structures. This work has important implications for crystal structure prediction.
However, it’s probably our 2020 paper that I’m most proud of because it demonstrates an approach to materials discovery that has been a vision of mine for quite a few years: combining chemical space exploration to identify new molecules with crystal structure prediction to evaluate their likely solid state properties.
What do you feel has been the most important development with your area of research since your first publication in Chemical Science in 2011?
The most important thing has been the increasing trust that people put in computational methods for studying materials. Even just a decade ago, there was a lot of scepticism surrounding methods like crystal structure prediction. This has changed, partly because of the improved methods that are now available, but also due to better communication of the limitations and uncertainties in computational predictions.
What do you hope to be able to contribute to the community through your new role as Associate Editor?
I have tried to keep up a broad level of knowledge of computational chemistry methods and their applications and I hope that I can use this to make informed and fair decisions on what to publish in Chemical Science. I’m really excited to see what people submit because there’s so much interesting work going on.
Why do you feel that researchers should choose to publish their work in Chemical Science?
I feel that I’m joining an editorial board that has done a great job in attracting the highest quality work and building a strong reputation for this flagship journal of the RSC. This means that people read Chemical Science when looking for exciting work. That’s important for researchers: knowing that you’re publishing your best work in a journal where it will be picked up quickly by the community. I think that this is particularly true in the area of computational chemistry, machine learning and AI applications in chemistry. The journal has been a great place for work in these areas that are of broad interest. The journal being Diamond Open Access is, of course, also a great thing. Researchers can make their work freely available without needing to find the budget to pay open access fees.
How do you see your field progressing in the next 10 years?
One big area will be the increasing integration of computational methods with experiments, where automation and robotics will play a big role. I’m looking forward to seeing more experiments where ideas are seeded by computational modelling and machine learning. I also hope that we see some artificial boundaries fall away, particularly between theoretical and experimental chemists. Of course, we need specialisation, but I want to see more people working across that boundary.
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