Using Machine learning to enable feedback controlled manufacture of self-assembled patterned materials

Achieving control over materials' structure at small length scales is a critical requirement to realise new technologies with improved performance in areas of energy generation, energy storage and healthcare. To give some examples, solar cells, batteries and sensors for medical diagnostics can all benefit from nano and micron scale structuring. An attractive way to produce such structures is via self-assembly, where materials arrange themselves into well-defined regular patterns. One way to access this behaviour is by drying a suspension or solution containing the material of interest. For example, if a suspension of particles in a liquid is spread onto a solid support and left to dry, under some conditions very regular, repeating "crystalline" arrangements will result. These arrangements possess optical properties that can be used for sensors, and serve as templates to make efficient electrodes for batteries and solar cell materials. Other examples of useful structure formation processes in drying solutions include complex networks that form from multi-component mixtures, and regular crystalline structures, both of which can enhance the performance of solar cells if present at certain specific length scales.

However, despite its simplicity and potential applications, at present self-assembly in drying solutions is mainly used as a research method to produce small quantities of material, and is not viewed as a routine manufacturing method. We believe that this is because self-assembly is highly sensitive to many parameters, which hampers reproducibility and requires time consuming optimisation to produce a given material, making it un-attractive for large scale manufacturing. Here, we plan to investigate if adding an automated control system to continuous self-assembly/solution structure based processes can overcome these obstacles. To implement the control system, we will make microscopic observations during self-assembly and use algorithms to adjust the manufacturing instruments parameters based on this feedback. This method will be used both to rapidly identify the parameters required to produce the ideal structure for a particular application, and also to maintain high quality uniform structure production during continuous manufacture of large amounts of material.

Despite feedback being well established as an effective way to control manufacturing processes in other sectors, this method has not yet been deployed for self-assembly due to difficulties in observing the structure forming process, and the challenges of implementing the required algorithms to beneficially adjust parameters. Here we will use advances in real time monitoring of drying films, together with expertise in "machine learning" computer methods that are able to build models for complex behaviour, to overcome these challenges.

Our vision is to develop a solution based manufacturing method that can reliably produce large quantities of patterned materials to enable new technologies to address pressing issues in energy generation, energy storage and healthcare. The resulting increased availability of materials that can improve the performance of photovoltaic cells, batteries and sensors can deliver considerable economic and societal benefits. In addition, the approach we are investigating utilises bottom up self-assembly, rather than high energy top down manufacture, enabling a concurrent reduction in environmental impact associated with high value goods production.

We also seek to inspire the public and increase awareness around the importance of developing efficient manufacturing methods to produce materials that can address environmental issues.