Predicting long-term materials ageing using reaction discovery and machine learning

预测有机聚合物材料在周围环境光热条件下的长期（数十年或更长时间）稳定性是一个独特的挑战，因为在这样的时间尺度上进行实验测试通常是不可能的或过于昂贵。在这个项目中，我们将把反应发现工具（Habershon小组）与机器学习能量计算方法（Maurer小组）合并，以开发动力学模型来预测有机聚合物材料的长期行为。然后，这些预测模型将有可能用于指导选择具有长期环境应用的定制特性的材料。

Predicting the long-term (decades or more) stability of organic polymeric materials under ambient environmental photothermal conditions is a unique challenge because experimental testing on such time-scales is often impossible or too expensive. In this project, we will merge reaction discovery tools (Habershon group) with machine-learning energy calculation methods (Maurer Group) to develop kinetic models to predict the long-term behaviour of organic polymeric materials. These predictive models will then have potential to be used to guide the choice of materials with tailored properties for long-term environmental applications.