Combining AI and automation towards the prediction of crystal structure landscapes

Solid sampling at the sub-milligram and milligram scale remains a major bottleneck for high-throughput chemistry or material sciences, as existing approaches rely on manual handling or slow deterministic microsampling that do not readily scale.

Here we present STORMS, an automated STOchastic Robotic MicroSampling system that enables fast, reliable and parallel sampling of solid materials at sub-milligram and milligram masses. 

Polymer–solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid screening assessments. While computer vision models hold significant promise to replace the reliance on human evaluation for inference, their adoption is limited by the lack of domain-specific datasets tailored, in our case, to polymer–solvent systems.

A first example of diglycolamide flow synthesis was developed, showcasing algorithm-accelerated reaction optimization and its potential to accelerate ligand discovery and enable autonomous, AIcontrolled manufacturing of critical materials. The increasing demand for manufacturing rare-earth elements (REEs) as critical materials has intensified research on sustainable synthesis of advanced extractants such as diglycolamides (DGAs).