Artificial Intelligence and Machine Learning to Crack Crystal Growth

**Crystal**_Grower_ Ltd是一家科学软件公司，从曼彻斯特大学分离出来，生产模拟晶体生长的软件。每个人都知道自然界中的晶体，钻石，盐，雪花，还有软体动物的外壳，甚至鲨鱼眼睛中的聚焦成分。人们可能不太了解的是，晶体是我们日常生活中使用的几乎所有功能材料的活性成分：药物，如阿司匹林和扑热息痛;计算机中的电子元件;用于氢经济的储氢材料或用于改善环境的大气中的二氧化碳。所有这些晶体都是通过在实验室中生长而产生的，代表了数十亿磅全球和英国制造市场。然而，为了精确地控制这些晶体的功能，首先关键的是在纳米/分子尺度上控制它们的生长。这确保了所生产的晶体具有所需的质量和功能特性。由 **Crystal**_Grower_ Ltd生产的软件通过模拟晶体生长方式以及在改变实验室条件时晶体生长的变化来帮助公司实现这一点，从而获得更好的生产结果。这种模拟在很大程度上依赖于模拟晶体与实验生产的晶体的比较，目前，必须在大量人为干预的情况下才能完成。目前这个项目的目的是开发基于图像的分类器，使我们的客户能够以更智能，自动化的方式处理大量数据。这将大大缩短处理模拟的时间，减少人为错误，特别是在大规模情况下，并最终实现更快速的洞察力。这将使我们的客户能够将更多的时间用于开发产品，而不是数据分析。图像分类技术还可以实现先进模拟转向技术的未来发展，减少解决方案的时间，同时消耗更少的计算资源。

**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.