Python中的微结构表征和重构：MCRpy
Microstructure Characterization and Reconstruction in Python: MCRpy
摘要：
微结构表征与重构（MCR）是增强和加速集成计算材料工程的重要前提。近年来，MCR研究取得了很大进展，但在缺乏灵活的软件平台的情况下，很难借鉴其他研究者的思想并进一步发展。为了解决这个问题，这项工作提出了MCRpy易于使用，可扩展和灵活的MCR。该软件平台可用作具有图形用户界面的程序、命令行工具和Python库。其核心思想是，微结构重建是制定为一个模块化和可扩展的优化问题。以这种方式，任何描述符都可以用于表征，并且可以使用任何用于重建的优化器来最小化组合任何描述符的任何损失函数。使用随机优化器，这导致了著名的Yeong-Torquato算法的变化。此外，MCRpy具有自动微分功能，可以利用基于梯度的优化器。在这项工作中，在简要介绍了基本概念之后，MCRpy的功能通过将其应用于典型的MCR任务来展示。最后，它示出了如何扩展MCRpy定义一个新的微观结构描述符，并很容易使用它进行重建，而无需额外的实施工作。
英文摘要：
Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently, however, in absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work presents MCRpy for easy-to-use, extensible and flexible MCR. The software platform that can be used as a program with graphical user interface, as a command line tool and as a Python library. The central idea is that microstructure reconstruction is formulated as a modular and extensible optimization problem. In this way, any descriptors can be used for characterization and any loss function combining any descriptors can be minimized using any optimizer for reconstruction. With stochastic optimizers, this leads to variations of the well-known Yeong-Torquato algorithm. Furthermore, MCRpy features automatic differentiation, enabling the utilization of gradient-based optimizers. In this work, after a brief introduction to the underlying concepts, the capabilities of MCRpy are demonstrated by exemplarily applying it to typical MCR tasks. Finally, it is shown how to extend MCRpy by defining a new microstructure descriptor and readily using it for reconstruction without additional implementation effort.
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