The Evolution of Active Droplets in Chemorobotic Platforms

There is great interest in oil-in-water droplets as simple systems that display astonishingly complex behaviours. Recently, we reported a chemorobotic platform capable of autonomously exploring and evolving the behaviours these droplets can exhibit. The platform enabled us to undertake a large number of reproducible experiments, allowing us to probe the non-linear relationship between droplet composition and behaviour. Herein we introduce this work, and also report on the recent developments we have made to this system. These include new platforms to simultaneously evolve the droplets’ physical and chemical environments and the inclusion of selfreplicating molecules in the droplets

A curious formulation robot enables the discovery of a novel proto-cell behaviour

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry

Development of a minimal photosystem for hydrogen production in inorganic chemical cells

Inorganic chemical cells (iCHELLs) are compartment structures consisting of polyoxometalates (POMs) and cations, offering structured and confined reaction spaces bounded by membranes. We have constructed a system capable of efficient anisotropic and hierarchical photo‐induced electron transfer across the iCHELL membrane. Mimicking photosynthesis, our system uses proton gradients between the compartment and the bulk to drive efficient conversion of light into chemical energy, producing hydrogen upon irradiation. This illustrates the power of the iCHELL approach for catalysis, where the structure, compartmentalisation and variation in possible components could be utilised to approach a wide range of reactions

A Closed Loop Discovery Robot Driven by a Curiosity Algorithm Discovers Proto-Cells That Show Complex and Emergent Behaviours

We describe a chemical robotic discovery assistant equipped with a curiosity algorithm (CA) that can efficiently explore a complex chemical system in search of complex emergent phenomena exhibited by proto-cell droplets. The CA-robot is designed to explore proto-cell formulations in an open-ended way with no explicit discovery or optimization target. By applying the CA-robot to the study of multicomponent oil-in-water proto-cell droplets, we discovered an order of magnitude more instances of interesting behaviours than possible with a random parameter search. Among them, a formulation displaying a sudden and highly specific response to temperature was discovered. Six modes of proto-cell droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR, which allowed the design of a payload release system triggered by temperature. This work shows how objective free search can lead to the discovery of useful and unexpected properties, with real-world applications in formulation chemistry.</p

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior

Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing &gt;7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone

Solution of hierarchical optimization problems

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