Scientists demonstrate chemical reservoir calculation using formosa reaction

Researchers from the Institute for Molecules and Materials at Radboud University, the Netherlands, have demonstrated that a complex self-organized network of chemical reactions can perform various computational tasks, such as nonlinear classification and prediction of complex dynamics.

The field of molecular computing interests researchers who wish to harness the computational power of chemical and biological systems. In these systems, chemical reactions or molecular processes act as a reservoir computer, transforming inputs into high-dimensional outputs.

The research, published in Naturewas led by Prof. Wilhelm Huck from Radboud University.

Researchers have exploited the potential that chemical and biological networks offer because of their complex computational capabilities. However, the implementation of molecular computing presents challenges in terms of engineering and design.

Instead of trying to create molecular systems to perform specific computational tasks, Prof. Huck and his team are exploring how naturally complex chemical systems can exhibit emergent computational properties.

“I am very interested in the chemical driving forces that led to the origin of life. In this context, we are looking for mechanisms by which chemical evolution can shape the properties of complex reaction mixtures. This research led us to consider how molecular systems can process information,” he explained to Phys.org.

Form reaction

The formose reaction is a chemical reaction that synthesizes sugars from formaldehyde in the presence of a catalyst, calcium hydroxide. This reaction was chosen because of its unique properties.

Prof. Huck explained, “Although chemistry may seem complex to outsiders, most reaction sequences are quite linear. The formic reaction is the only example of a self-organizing reaction network with a highly nonlinear topology, containing multiple positive feedback loops and negative.”

In other words, the reaction is not straightforward and produces numerous intermediate compounds which react further to form new compounds. These dynamic reactions can result in a diverse set of chemical species and are nonlinear in nature.

Additionally, the network includes positive feedback loops that amplify feedback outputs and negative feedback loops that attenuate feedback outputs.

The network is known as “self-organizing” because it naturally evolves and responds to chemical inputs without the need for external intervention, producing a diverse range of outputs.

Computational capabilities emerge from the inherent properties of the network rather than being explicitly programmed, making computation highly flexible.

Implementation of the tank computer

The researchers used a continuous stirred tank reactor (CSTR) to implement the formosa reaction. The input concentrations of four reactants—formaldehyde, dihydroxyacetone, sodium hydroxide, and calcium chloride—are controlled to modulate the behavior of the reaction network.

The resulting molecule is identified using a mass spectrometer, which allows them to track up to 106 molecules. This configuration can be used to make calculations, where the concentrations of the reactants are the input values ​​for any function to be calculated.

But first, the system must be trained to find the result of this calculation, which is done using a set of weights.

“We need to find a set of weights that convert the traces in the mass spectrometer to the exact calculation value. This is a linear regression problem and is computationally simple. Once done, the tank computer calculates the score for this function for any new data”, explained Prof. Huck.

Weights are coefficients that determine the influence of each input on the output. This training step is essential as it allows the reservoir to learn and predict how changes in the input affect the output so that it can predict the output for a new set of inputs.

Computational skills

The researchers used the tank’s computer to perform several tasks. The first was to perform nonlinear classification tasks. The tank computer can emulate all Boolean logic gates and even handle more complex classifications such as XOR, checkers, circles and sine functions.

The team also showed that it could predict the behavior of a complex model of the E. coli metabolic network, accurately capturing the linear and nonlinear responses to fluctuating inputs at different concentration ranges.

Additionally, the system demonstrated the ability to predict future states of a chaotic system (Lorenz attractor), accurately predicting two of the three input dimensions several hours into the future.

The research team also found that certain types of chemicals in the system exhibit short-term memory, retaining information about past inputs.

They also demonstrated a proof of concept for a fully chemical readout using colorimetric reactions, showing how the state of the system could be interpreted without electronic measuring devices.

In other words, the state of the system can be interpreted using color changes from chemical reactions, eliminating the need for electronic measuring devices.

The origin of life, neuromorphic computing and beyond

This new approach to molecular computing could bridge the gap between artificial systems and the information processing capabilities of living cells.

It suggests a more scalable and flexible approach to molecular computing, opening up possibilities for creating autonomous chemical systems that can process information and respond to their environment without external electronic control.

Prof. Huck expressed his team’s interest in this area, saying, “Can we introduce reservoir computing into chemical systems that sense their environment, process this information, and take appropriate action?

“This would require coupling the tank with other elements that could translate the brain’s chemical output into some form of mechanical feedback or an interaction with living cells, for example.”

The research also has intriguing implications for the origins of life. The emergent computational properties of this relatively simple chemical system may provide insights into how early biological systems may have developed information-processing capabilities.

Prof. Huck mentioned that this was his main motivation for studying reservoir calculus.

The research team also sees potential in neuromorphic computing, which mimics the neural structure and functioning of the human brain to improve efficiency and computing power.

“We are very interested in exploring the technological limits of computing power of the formose reservoir computer – this is an ongoing research in collaboration with IBM Zurich. Reservoir computing is an example of neuromorphic computing, which has gathered interest as it is expected to consume less energy than conventional computers”, explained Prof. Huck.

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