Complexity to clarity: detecting, identifying and analysing complex materials systems at the molecular level with machine learning

该项目旨在开发机器学习方法，以表征、分类和预测复杂材料系统的特性，包括纳米结构功能表面、多层和涂层。该项目希望通过使用自组织地图和创新的高光谱数据可视化技术，为材料类型、来源、起源和制造的深层和精确的法医调查提供一个框架。该项目的预期结果包括增强研究未知材料的能力，解构复杂的失效和降解过程，预测结构-功能关系。这将为解决新兴材料技术和工艺的威胁提供显著的好处。

This project aims to develop machine learning methodologies to characterise, classify and predict the properties of complex materials systems including nanostructured functional surfaces, multilayers and coatings. This project expects to generate a framework for deep level and precise forensic investigation of materials type, source, origin and manufacture, employing self organising maps and innovative hyperspectral data visualisation. Expected outcomes of this project include enhanced capacity to investigate unknown materials, deconstruct complex failures and degradation processes and predict structure - function relationships. This will provide significant benefits in addressing threats from emerging materials technologies and processes.