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

从基础和应用（即工业）的角度来看，理解复杂的化学和物理软物质系统的行为是很重要的。理解复杂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，构建相图是一项艰巨的任务，可能需要数年时间才能完全验证给定的系统。我们的团队对通过将小角中子散射（SANS）和其他散射技术与微流体平台相结合来探测和绘制复杂聚合物和表面活性剂混合物的相图感兴趣。1 -3最近，微流体的出现加速了相图。4 -6此外，在当前的文献中已经有关于利用机器学习优化相位映射的例子。7 - 10考虑到这一点，描述了组合微流体、在线分析（SANS和其他散射技术）和主动学习优化算法的基本原理。首先，简要介绍了软物质和微流体的概念。第二，探测软物质的方法，特别是散射技术，描述。一个简短的理论多维相图覆盖和文献的例子，相位映射的优化。

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.