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Research

Our group works at the interface of supramolecular chemistry, physical organic chemistry, and chemical automation. Our aim is to build predictive, data-rich workflows that allow us to design, make, and understand functional molecular systems more efficiently. We focus on experimentally accessible targets – systems that can be synthesised and studied using automated platforms – and use computation and data to drive discovery. We are interested in both function (molecular recognition, catalysis) and process (how to design, discover, and understand complex chemical behaviour), across three core research themes.

Automation and high-throughput experimentation

A core strength of the group is the integration of automation and HTE into supramolecular and physical organic chemistry. We build and use custom workflows for automated synthesis and analysis, particularly for:

  • Dynamic library generation and screening, using robotic platforms and advanced characterisation methods (e.g., NMR and LC-MS) to probe product distributions and binding.
  • Mechanistic studies of complex reactions, especially in systems with multiple equilibria or encapsulated intermediates, where standard kinetic experiments would be impractically slow.
  • Autonomous data collection and interpretation, using custom code for automated data analysis and closed-loop automation.

Example publications:

T. Dai, S. Vijayakrishnan, F. T. Szczypiński, J.-F. Ayme, E. Simaei, T. Fellowes, R. Clowes, L. Kotopanov, C. E. Shields, Z. Zhou, J. W. Ward and A. I. Cooper, Autonomous mobile robots for exploratory synthetic chemistry, Nature, 2024, 635, 890–897.

C. E. Shields, T. Fellowes, A. G. Slater, A. I. Cooper, K. G. Andrews and F. T. Szczypiński, Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation, Chem. Commun., 2024, 60, 6023–6026.

Digital supramolecular chemistry

We are working on bringing cheminformatics, molecular modelling, and machine learning into supramolecular design – much like they are used in drug discovery. We favour systems that are robust and modular, so they can be handled by robots, iterated on quickly, and used by collaborators. Our high-throughput platforms generate rich datasets that allow structure–function relationships to emerge naturally, even in noisy or complex systems:

  • Dynamic combinatorial libraries for self-sorting and guest binding.
  • Molecular modelling to predict structure and energy landscapes.
  • Machine learning to learn from experimental data and guide library design.

Example publications:

F. T. Szczypiński, S. Bennett and K. E. Jelfs, Can we predict materials that can be synthesised?, Chem. Sci., 2021, 12, 830–840.

V. Abet, F. T. Szczypiński, M. A. Little, V. Santolini, C. D. Jones, R. Evans, C. Wilson, X. Wu, M. F. Thorne, M. J. Bennison, P. Cui, A. I. Cooper, K. E. Jelfs and A. G. Slater, Inducing Social Self-Sorting in Organic Cages To Tune The Shape of The Internal Cavity, Angew. Chem. Int. Ed., 2020, 59, 16755–16763.

Molecular recognition and function

We’re fascinated by how synthetic molecules can selectively bind, transform, or respond to guests, especially where structure-function relationships are subtle or emergent. Our interests include:

  • Artificial receptors that selectively bind specific molecular or multi-component guests, designed using computational tools and validated with binding experiments.
  • Transition-state stabilisation and catalysis, where receptors act like molecular flasks—promoting reactions through spatial preorganisation and energetic steering.
  • Stimuli-responsive assemblies, including light-, pH-, or guest-triggered systems capable of switching states, releasing bound guests, or changing selectivity.

Example publications:

F. T. Szczypiński, L. Gabrielli and C. A. Hunter, Emergent supramolecular assembly properties of a recognition-encoded oligoester, Chem. Sci., 2019, 10, 5397–5404.

W. J. Ramsay, F. T. Szczypiński, H. Weissman, T. K. Ronson, M. M. J. Smulders, B. Rybtchinski and J. R. Nitschke, Designed Enclosure Enables Guest Binding Within the 4200 Å3 Cavity of a Self-Assembled Cube, Angew. Chem. Int. Ed., 2015, 54, 5636–5640.