During this period, the CORENET consortium has shown that the formose and glyconitrile CRN compositions are highly dynamic and non-linearly dependent on different variable conditions. Additional chemical inputs are currently being explored for both cases, with promising results. Hence, these CRNs can in principle be used to perform advanced computing tasks. We have actually widen the available tools for massive and on-line output data extraction from GC-MS and tims-TOF to techniques such as FT-IR and UV-Vis spectroscopy, which are fast, accurate and cheap. This enables to monitor the chemical evolution and the steering properties of the formose and the glyconitrile CRNs in chemical reactors such that information can be processed by the chemical systems. This concept requires the chemical input and the reaction conditions to be well controlled, which is done by automating all chemical actions in a scripting environment. Finally, ways to solve nonlinear classification tasks with the physical reservoir computer computing tasks with specific chemical action sequences have been developed, which are highly generic and allow for first computing tasks with chemical systems. The protocols developed allow analytical algorithms to be used for generation of feature and composition vectors in a machine-readable format. First examples for classification, complex dynamics and nonlinear predictions were presented and demonstrated to outperform tailored machine learning (ML) models, in particular for nonlinear classification. This ML tool implementation marks a significant step toward data-driven exploration and prediction within CRNs, providing a scalable solution for processing complex experimental data. The establishment of a pipeline for ML-enhanced reservoir computing, more complex computing tasks can be envisioned in the future.
