Optimisation of multi-component phase mapping with machine learning and automation

通过机器学习和自动化优化多组分相图

• 批准号: 2194188
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2194188
• 关键词: Optimisation; multi; component; phase; mapping

Understanding the behaviour of complex chemical and physical soft matter systems is important from fundamental and applied (i.e. industrial) points of view. A way of understanding complex chemical and physical systems is characterising their thermodynamic behaviour. This can be described with the aid of phase diagrams. However, constructing a phase diagram is a laborious task and can take years to fully characterise a given system.Our group is interested in probing and phase-mapping complex polymer and surfactant mixtures with small angle neutron scattering (SANS) and other scattering techniques by coupling these techniques to microfluidic platforms.1-3 Recently, the advent of microfluidics has accelerated phase mapping.4-6 In addition, there have been examples in current literature on optimisation of phase mapping with machine learning.7-10With that in mind, the rationale behind combining microfluidics, in-line analytics (SANS and other scattering techniques) and active learning optimisation algorithm is described. First, the concept of soft matter and microfluidics is briefly introduced. Second, ways of probing soft matter, particularly with scattering techniques, described. A brief theory of multi-dimensional phase diagrams is covered and literature examples for optimisation of phase mapping given.

从基本和应用(即工业)的观点来看，理解复杂的化学和物理软物质系统的行为是重要的。理解复杂的化学和物理系统的一种方法是描述它们的热力学行为。这可以借助相图来描述。然而，构建相图是一项费力的任务，可能需要数年时间才能完全表征给定的体系。我们团队对探测和相图复杂的聚合物和表面活性剂混合物与小角中子散射(SANS)和其他散射技术感兴趣，将这些技术结合到微流体平台上。1-3最近，微流体的出现加速了相图的绘制。4-6此外，当前文献中已经有使用机器学习优化相图的例子。7-10考虑到这一点，我们描述了结合微流体、在线分析(SANS和其他散射技术)和主动学习优化算法的原理。首先，简要介绍了软物质和微流体的概念。其次，描述了探测软物质的方法，特别是利用散射技术。涵盖了多维相图的简要理论，并给出了优化相图的文献例子。