This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green’s function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time.

本文回顾并提出了一种数据科学框架，该框架可用于捕获和传达时空多尺度模拟中有关材料结构演变的关键信息。这种方法称为MKS（材料知识系统）框架，先前已成功应用于空间多尺度模拟中主要捕获微观结构与属性的链接。本文通过允许引入不同的基本功能来概括该框架，并探讨了它们在建立所需的过程结构属性（PSP）链接方面的潜在好处。通过使用Cahn-Hilliard仿真作为示例案例研究，证明了这些新开发成果，在该案例中，结构演化的预测速度比优化的数值积分算法快了三个数量级。这项研究表明，MKS本地化框架提供了一种替代方法，用于学习通过基于格林函数的影响核而不是微分方程表示的数值模型中的潜在嵌入式物理学，并且在数值积分方案难以优化的问题中可能提供显着的计算优势。 。通过此扩展，我们现在已经建立了一个全面的框架，用于捕获PSP链接，以便在空间和时间上进行多尺度材料建模和仿真。并在数值积分方案难以优化的问题中潜在地提供了显着的计算优势。通过此扩展，我们现在已经建立了一个全面的框架，用于捕获PSP链接，以便在空间和时间上进行多尺度材料建模和仿真。并在数值积分方案难以优化的问题中潜在地提供了显着的计算优势。通过此扩展，我们现在已经建立了一个全面的框架，用于捕获PSP链接，以便在空间和时间上进行多尺度材料建模和仿真。