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“The Cavendish Women in Physics have made unparalleled contributions to the Cavendish Laboratory’s extraordinary history of discovery and innovation in Physics and continue to do so.”
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Artist’s illustration of the entrance of the Ray Dolby Centre. Credit: Jestico + Whiles
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These fields encompass a variety of research groups, each with its own scientific aims and ambitions but united by two common goals:
Click on each of the themes to learn more about our researchers and their work.
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Our group leads the AI for Materials team, winner of the Royal Society of Chemistry's 2025 Materials Chemistry Horizon Prize. The team's chemistry-aware AI tools (open-source software, databases and language models) are changing how scientists design, analyse and validate new materials. A topical full demonstration of data-driven materials discovery ran in under a year, against a global average of 20, and the approach is now supporting research across disciplines.
Watch the showcase video
Professor Cole was awarded the 2020 Royal Society's Clifford Paterson Medal and Lecture for developing photo-crystallography and discovering new nonlinear optical materials and light-harvesting dyes through molecular design rules. In the lecture, she sets out a 'design-to-device' pipeline: large-scale data mining predicts promising new chemicals, which are then made and tested for the energy sector.
Watch Prize lecture on YouTube
Professor Cole received the American Crystallographic Association's B. E. Warren Diffraction Physics Award, given once every three years, for pioneering the photo-crystallography technique, which captures the structure of a crystal while light is shone on it. In a collaboration with Argonne National Laboratory, Professor Cole studied a family of ruthenium compounds that physically bend, peel or fractured when illuminated, acting as optical actuators. Understanding how the change in structure produces this behaviour is what would let us engineer these materials deliberately, for uses from optical data storage to quantum technologies.
Watch Prize lecture on YouTube
Getting any molecule (say, a catalyst) to market for industrial use can take decades. AI tools and models can expedite this pipeline if they have quality (labelled) experimental data from which to learn, but running such experiments is slow and expensive. Our group's answer: much of the data are already there, but buried in published papers in a way computers cannot read. ChemDataExtractor, our custom 'chemistry-aware' text-mining tool, reads the chemistry like a professional chemist and extracts relevant data. It provides a solution to one of the biggest bottleneck in AI-driven materials discovery.
Read the article in Chemistry World
Modern LLMs are extremely powerful, but training them from scratch invites immense computational and environmental costs. Most research labs do not have access to such computing facilities. Our group's answer: skip the pretraining. Using ALCF's Polaris supercomputer, we turned our custom materials databases into question-and-answer pairs and used them to fine-tune small language models (SLMs). These domain-specific SLMs can match or beat far larger ones on materials-related questions, and you can run them on a few GPUs or your own computer.
Read the ALCF feature
The digital world runs on two states: on and off. Yet our group has made a crystal that exhibits three distinct states. The crystal is a ruthenium compound that can be switched with only a green light and liquid-nitrogen cooling. It converts completely between three molecular states (photoisomers), and fully reverses upon warming. In earlier works, materials managed only partial conversion, which limited their usefulness. The discovery of such precise control opens a route to optical data storage, photonic circuits and quantum computing hardware built on three-state logic.
Read the Cavendish news story
The Henry Royce Institute, the UK's national centre for advanced materials research and innovation, featured the group's three-state molecular switch and linked this study to its Atoms to Devices (A2D) programme. Professor Cole is The Royce Challenge Lead for AI in Materials Discovery, Characterisation and Application.
Read the Royce news item
Minglei Zhang, a PhD student in the group, was featured by the Cavendish Laboratory for the 2026 International Day of Women and Girls in Science, discussing why representation matters in AI research. Her point is a practical one: different perspectives help researchers notice unmet needs, build better datasets, and avoid blind spots in AI systems. These issues are particularly important in research areas such as developing biomedical materials for women's health.
Watch on Instagram
In this UK Neutron and Muon Source profile, Prof Cole describes going with what she liked: a school competition that took her to a science festival, a final-year crystallography project, a PhD half-spent in Grenoble, and Open University modules in maths and engineering taken alongside the job, one of which became a degree specialising in AI. A second PhD, in physics at Cambridge, came later, while she was already a research fellow supervising students of her own. She credits chance encounters and good mentors, and says she owes the same to her own students.
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Prof Cole talks to London Business School about an unusual route into research: nearly an archivist, caught by crystallography only at the end of her undergraduate chemistry degree, then two PhDs and further degrees in maths, engineering, and international strategy and diplomacy. She explains why she came to not rely on serendipitous discovery and how design-to-device pipelines became her alternative. Her best ideas, she says, arrive halfway up a mountain.
Read the interview