Enhancing Machine Learning with Physical Constraints to Predict Microstructure Evolution

去混合是自组装最普遍的例子之一，经常发生在复杂的流体和生命系统中。它使多相聚合物合金和复合材料的开发成为可能，用于复杂的应用，包括结构航空航天部件，柔性太阳能电池和过滤膜。在每种情况下，上级功能性都来自于微观结构，其预测未能保持与合成和配方进步的步伐。非平衡统计物理学、扩散和流变学的相互作用导致了具有重叠时间和长度尺度的多个过程，这阻碍了总体理论框架的发现。因此，我们在寻找新材料时继续严重依赖试验和错误。我们的目标是引入一种强大的新方法来建模非平衡软物质，将基于观察的机器学习的概念主义与基于物理学的基本概念主义相结合。我们将通过解决将物理系统的先验知识纳入概率学习规则的更广泛挑战来开发机器学习的新方法，通过使用预测工具来改变我们控制和定制微观结构的能力。我们的目标是创建经验学习引擎，受物理定律的约束，将使用显微镜，断层扫描和散射数据进行训练。在这项可行性研究中，我们将专注于概念验证，探索模型混合物的温度/成分参数空间，为我们使用物理信息机器学习自动化和加速下一代应用的实验材料发现的雄心奠定基础。

De-mixing is one of the most ubiquitous examples of self-assembly, occurring frequently in complex fluids and living systems. It has enabled the development of multi-phase polymer alloys and composites for use in sophisticated applications including structural aerospace components, flexible solar cells and filtration membranes. In each case, superior functionality is derived from the microstructure, the prediction of which has failed to maintain pace with synthetic and formulation advances. The interplay of non-equilibrium statistical physics, diffusion and rheology causes multiple processes with overlapping time and length scales, which has stalled the discovery of an overarching theoretical framework. Consequently, we continue to rely heavily on trial and error in the search for new materials. Our aim is to introduce a powerful new approach to modelling non-equilibrium soft matter, combining the observation based empiricism of machine learning with the fundamental based conceptualism of physics. We will develop new methods in machine learning by addressing the broader challenge of incorporating prior knowledge of physical systems into probabilistic learning rules, transforming our capacity to control and tailor microstructure through the use of predictive tools. Our goal is to create empirical learning engines, constrained by the laws of physics, that will be trained using microscopy, tomography and scattering data. In this feasibility study, we will focus on proof-of-concept, exploring the temperature / composition parameter space for a model blend, building the foundations for our ambition of using physics informed machine learning to automate and accelerate experimental materials discovery for next generation applications.