The discovery of functional nanomaterials for sustainable technologies is challenged by vast multidimensional chemical spaces and by the difficulty of closing the loop between synthesis, characterization, data interpretation, and decision-making. This challenge is particularly acute for nanoparticles used in catalysis and energy-related applications, because their properties and performance depend sensitively on chemical composition, atomic structure, and nanoparticle size and shape.

What a self-driving laboratory can learn ultimately depends on the information it is allowed to learn from. In this context, information fidelity reflects both data quality and relevance to the target property or structure.

When total X-ray scattering is combined with atomic pair distribution function (TXS-PDF), the reciprocal-space total scattering data are Fourier transformed into real-space atomic pair correlations, enabling local atomic structure to be probed even when nanoparticles are too small or disordered for conventional diffraction analysis [1]. 
High-fidelity TXS-PDF data are typically acquired at synchrotron X-ray sources, where high-brilliance X-ray beams enable rapid acquisition of structure-sensitive data. ScatterLab has so far been established as a synchrotron-based self-driving laboratory [2]. Unfortunately, synchrotron access is limited. This motivates a transition from synchrotron-based operation toward in-house workflows using laboratory X-ray sources.

While TXS-PDF is powerful for atomic structure characterization, small-angle X-ray scattering (SAXS) provides a more direct and robust probe for particle size and shape [3]. Together with rapid optical probes such as UV-Vis spectroscopy, TXS-PDF and SAXS provide complementary information on optical response, atomic structure, and nanoscale morphology.

One of the greatest assets of ScatterLab is our robotic modular experimentation platform (MODEX). MODEX automates solution-based chemical synthesis through modular unit operations such as dispensing, mixing, illumination, and sample transfer. This modularity makes the platform configurable across synthesis protocols and transferable between characterization instruments.

Bayesian optimization turns ScatterLab from an automated platform into an autonomous laboratory by selecting the next synthesis parameters based on previous experimental outcomes. In our current implementation, the optimization target is the agreement between experimental and simulated scattering patterns, quantified using the mean squared error.

ScatterLab uses robotics and Bayesian optimization to close the loop around structure-sensitive feedback. The next challenge is moving from scarce synchrotron measurements to in-house multimodal characterization, where the autonomous laboratory learns from fit-for-purpose information relevant to functional nanomaterials for catalysis and energy applications.