Artificial Intelligence and Machine Learning to Crack Crystal Growth

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**Crystal**_Grower_ Ltd is a scientific software company, spun out from The University of Manchester, that produces software to model how crystals grow. Everyone is aware of crystals in nature, diamonds, salt, snowflakes but also the shells of molluscs and even the focussing components in shark eyes. What people may be less aware of is that crystals are the active components of almost every functional material that we use in our everyday life: pharmaceuticals, such as aspirin and paracetamol; the electronic elements inside your computer; the materials used for storing hydrogen for a hydrogen economy or removing carbon dioxide from the atmosphere for an improved environment. All these crystals are produced by growing them in the laboratory and represent a multi-billion pound global and UK manufacturing market. However, in order to control precisely the functionality of these crystals it is critical first to control their growth at the nano/molecular scale. This ensures that the crystals are produced with the required quality and functional properties. The software produced by **Crystal**_Grower_ Ltd helps companies do exactly that by simulating how crystals grow and how this growth changes upon altering laboratory conditions thereby leading to better production outcomes.Much of this simulation relies on comparison of simulated crystals with those produced experimentally which, at present, must be done with significant human intervention. The purpose of this current project is to develop image-based classifiers that would enable our clients to process substantial volumes of data in a more intelligent, automated way. It would take significantly less time to process simulations, reduce human error, particularly at scale, and ultimately enable more rapid insights. This would allow our clients to spend more time focused on developing their products, instead of data analytics. Image classification technology can also enable the future development of advanced simulation steering techniques, reducing the time to solution, whilst consuming fewer computational resources.