Machine Learning for Computational Water Treatment

内分泌干扰物(EDCs)影响动物的激素系统，模仿动物体内自然产生的激素(如雌激素或睾酮)的影响，并阻止它们的作用。这些化学物质的影响是广泛的，包括生殖障碍和发育问题。不幸的是，各种各样的化合物有可能扰乱内分泌系统，包括药品(例如抗生素)、个人护理产品(例如除臭剂)和制造原材料(例如双酚)。虽然这些化合物对人类健康的全面影响尚不清楚，但它们在饮用水中的去除是水处理中出现的一个问题，随着更多内分泌细胞的发现，这个问题只会变得更加重要。从饮用水中去除特定EDC的最佳方法并不总是显而易见的，标准做法是筛选不同的可能方法以找到最佳方法。就金钱和时间而言，这可能是非常昂贵的，而且对于一个饮用水来源最好的方法可能并不总是对另一个成分不同的来源最好。该项目利用计算化学和机器学习(ML)的力量来加快寻找去除EDC的材料，从原子模拟开始研究硅中的水去污，并结合实验室实验的结果。这项工作的高潮将是开发一种高效而强大的ML框架，该框架可以预测一种材料从饮用水中去除内分泌干扰物的能力，通过建议要重点关注的候选材料节省大量实验时间，并允许水管理行业迅速采取行动处理新发现的内分泌干扰物。

Endocrine-disrupting chemicals (EDCs) affect the hormone systems of animals, mimicking the effects of naturally occurring hormones (such as oestrogen or testosterone) in animals and blocking their action. The effects of these chemicals are wide-ranging and include reproductive failure and developmental problems. Unfortunately, a huge variety of compounds have the potential to disrupt the endocrine system, including pharmaceuticals (e.g. antibiotics), personal care products (e.g. deodorants) and raw materials for manufacturing (e.g. bisphenols). While the full effect of these compounds on human health is not yet known, their removal from drinking water is an emerging problem in water treatment, and one that only becomes more important as more EDCs are discovered. The best way to remove a given EDC from drinking water is not always obvious, and the standard practice is to screen different possible methods to find the optimum. This can be very costly in terms of both money and time, and the method that is best for one source of drinking water may not always be best in another source whose composition is different.This project harnesses the power of computational chemistry and machine-learning (ML) to speed up the search for materials for EDC removal, beginning with atomistic simulations to study water decontamination in silico, in tandem with the results of laboratory experiments. The culmination of this work will be the development of an efficient and robust ML framework that can predict the ability of a material to remove an endocrine disruptor from drinking water, saving a significant amount of experimental time by suggesting candidate materials to focus on, and allowing the water management industry to act quickly to deal with newly discovered EDCs.