Publication date: 15th December 2025
The discovery and synthesis of functional materials underpin advances in energy conversion, catalysis, and sustainable technologies. However, identifying optimal synthesis protocols is still dominated by trial-and-error experimentation. Nanoparticles are particularly challenging to synthetically control due to the large number of tunable parameters and the requirement for advanced structural characterisation. At the same time, their large surface areas and atomically defined arrangements make them highly promising candidates for next-generation energy technologies.
In this talk, I will present recent progress towards self-driving laboratories (SDLs) for inorganic nanoparticle synthesis, where robotic experimentation, synchrotron-based total scattering, and machine learning (ML) are integrated into closed-loop discovery platforms.
I will describe proof-of-concept SDL experiments in which robotic synthesis was optimised by a Bayesian optimisation algorithm directly informed by experimental scattering data. Within four days of synchrotron beamtime, the system autonomously developed synthesis protocols for target 5 nm decahedral and
10 nm face-centred cubic gold nanomaterials. By coupling our SDL with ML-driven interatomic potentials, we foresee that one can first identify promising material candidates in silico before they are realised synthetically. By bridging chemistry, automation, and AI, this research aims to transform materials discovery; moving from intuition-driven exploration to autonomous, knowledge-guided design at scale.
