Complex chemical reaction networks for breakthrough scalable reservoir computing

Projektinformationen

CORENET

ID Finanzhilfevereinbarung: 101046294

DOI

10.3030/101046294

Projekt abgeschlossen

EK-Unterschriftsdatum 2 Februar 2022

Startdatum 1 April 2022

Enddatum 31 März 2026

Finanziert unter

• The European Innovation Council (EIC)

Gesamtkosten

€ 2 047 125,00

EU-Beitrag

€ 2 047 125,00

Koordiniert durch

UNIVERSIDAD AUTONOMA DE MADRID
Spain

Periodic Reporting for period 2 - CORENET (Complex chemical reaction networks for breakthrough scalable reservoir computing)

Berichtszeitraum: 2023-04-01 bis 2024-09-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Chemical synthesis has traditionally been a discipline that was part clever application of accumulated knowledge and part “art”. In recent years, a revolution has been gathering pace with the introduction of robotics, microfluidics, integrated analytics, and AI-based integration of data, leading to programmable synthesis of molecules using computer-controlled devices. This paradigm change opens up new opportunities to push chemistry beyond the synthesis of molecules to the synthesis of chemical reaction networks (CRNs), which in living organisms control all essential processes.
CORENET aims at construct chemical systems that can perform high-level computation based on chemical reactions. The project will implement reservoir computing based on the compartmentalisation of reaction networks with increasing complexity in microfluidic flow reactors.
The main advantage of CRNs for computing is that they are able to generate vast compositional chemical spaces, allowing them to process information and regulate their responses in a ‘metabolic’ way. The project will bring new knowledge bridging system chemistry, microfluidics technology and computational science and may revolutionize patient treatment via in situ synthesis of drug molecules.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

CORENET will have a significant impact in various areas of systems chemistry, CRNs and reservoir computing (RC), and it has been progressing in line with the planned outcomes.
Looking at the formose CRN, precise data, analysis and important new concepts in reaction pathway self-organization are of high relevance to the prebiotic chemistry community. The framework developed using the formose reaction as a model can be transferred to other CRNs, allowing the rationalisation of complex reaction outcomes and the inspiration of detailed hypotheses for origin of life scenarios that consider the dynamic and out-of-equilibrium properties of the prebiotic environment. With an established reservoir computing capacity of the formose CRN network, we can now demonstrate a range of complex non-linear classifications. The fabrication of integrated Si-based CSTRs has scientific impact as it allows for further integration and robust and reproducible data collection. The fabrication methods include possible patentable inventions with respect to embedding electrodes and heating elements.

Representation of CORENET’s approach to build a computing system based on complex reaction networks.