Year 1 timetable

Title: Prediction-led Discovery of Functional Molecular Organic Crystals

Speaker: Graeme Day

Abstract: The seminar will explore the application of crystal structure prediction (CSP) methods to the inverse design of crystalline molecular materials with targeted properties. There has been impressive recent progress in computational methods for predicting crystal structures from first principles, making use of efficient methods for exploring energy landscapes coupled with accurate evaluations of the stability of computer-generated crystal structures [1]. Given these advances, there is a current focus on how to make the best use of CSP to identify the most promising molecules to deliver a desired property. Our goal is to build a computational framework that integrates structure prediction, property prediction and methods to explore the chemical space of possible molecules [2]. Examples from the area of porous materials will demonstrate how computation-led research has resulted in the discovery of unusual materials with extraordinary properties. [3, 4] We are also developing how these computational methods will interact with automation in the materials chemistry lab, where promising molecules are screened to realise predicted structures [5, 6], with a long-term aim of closed-loop materials discovery.

[1] Predictive crystallography at scale: mapping, validating, and learning from 1000 crystal energy landscapes, Taylor, C. R., Butler, P. W. V., and Day, G. M., Faraday Discussions, 256, 434-458 (2025)

[2] Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery, Cheng, C. Y. and Day, G. M., Chemical Science, 11, 4922 (2020).

[3] Pulido, A., Chen, L., Kaczorowski, T., Holden, D., Little, M. A., Chong, S. Y., Slater, B. J., McMahon, D. P., Bonillo, B., Stackhouse, C., Stephenson, A., Kane, C. M., Clowes, R., Hasell, T., Cooper, A. I. and Day, G. M., Nature, 543, 657 (2017)

[4] Porous isoreticular non-metal organic frameworks, O’Shaughnessy, M., Glover, J., Hafizi, R, Barhi,M., Clowes, R, Chong, S. Y., Argent, S. P., Day, G. M. and Cooper, A. I., Nature 630, 102–108 (2024).

[5] Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights, Cui, P., McMahon, D. P., Spackman, P. R., Alston, B. M., Little, M. A., Day, G. M. and Cooper, A. I., Chemical Science, 10, 9988 (2019).

[6] Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry, Lunt, A. M., Fakhruldeen, H., Pizzuto, G., Longley, L., White, A., Rankin, N., Clowes, R., Alston, B., Gigli, L, Day, G.. M., Cooper, A. I. and Chong, S. Y., Chemical Science, 15, 2456-2463 (2024)

Bio: His research concerns the development of computational methods for modelling the organic molecular solid state. A key focus of this work is the prediction of crystal structures from first principles; his research group applies these methods in a range of applications, including pharmaceutical solid form screening, NMR crystallography and computer-guided discovery of functional materials.

Graeme is the author or co-author of over 150 publications, including 5 book chapters. He acts as Associate Editor for the Royal Society of Chemistry (RSC) flagship journal Chemical Science, and serves on the advisory board for the RSC’s journals Molecular Systems Design and Engineering and RSC Mechanochemistry, is on the steering committee of the UK Materials Chemistry High End Computing Consortium and is a member of the EPSRC peer review college. Graeme was awarded the CCDC Chemical Crystallography Prize for Younger Scientists in 2006 and the Molecular Graphics & Modelling Society, Silver Jubilee Prize in 2008, both for his contributions to crystal structure prediction methodologies, and the 2023 RSC Corday-Morgan Prize for pioneering the development of computational methods for guiding the discovery of functional molecular crystals. His current research is funded by the Engineering and Physical Sciences Research Council (EPSRC), the European Research Council (ERC) and a range of industrial collaborations. He leads the ERC Synergy Grant ADAM – Autonomous Discovery of Advanced Materials (2020-2027), combining predictive computational methods, automation of materials discovery and robotics.