npj: 材料微结构设计—图形的威力

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材料的微结构极大地影响着材料的质量、电荷和反应速率传递。微结构设计或形态发生，是对定制微结构进行识别、实现所需材料性能的最大化，是决定材料微结构布局、实现性能最优化的决策过程。

以图形表示微结构，并以数学方式映射它们的属性，可以帮助改进材料属性。目前，形态发生方法在微结构表示阶段和性质评估阶段的计算都过于复杂。来自美国爱荷华州立大学和布法罗大学的研究团队，用标记的、加权的、无向图来表示结构，简化了该优化过程。在此基础上，再以通用物理图形描述符（如路径长度、域的大小）和感兴趣属性的加权函数创建“替代”模型。他们以此作为形态发生的工具，演化了识别约束和各向异性约束的微结构。以面向光伏应用的优化形态设计为例，展示出显著改善的短路电流（与传统的体异质结形态相比改善了68％），发现厚一些的薄膜（250 nm）可用于收集更多的入射光能量。这种方法虽然只作为改进有机太阳能电池的新设计方法，但也可以扩展应用于电池电极、生物传感器等相关领域的设计。

该文近期发表于npj Computational Materials 4: 50 (2018)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Microstructure design using graphs 

Pengfei Du, Adrian Zebrowski, Jaroslaw Zola, Baskar Ganapathysubramanian & Olga Wodo 

Thin films with tailored microstructures are an emerging class of materials with applications such as battery electrodes, organic electronics, and biosensors. Such thin film devices typically exhibit a multi-phase microstructure that is confined, and show large anisotropy. Current approaches to microstructure design focus on optimizing bulk properties, by tuning features that are statistically averaged over a representative volume. Here, we report a tool for morphogenesis posed as a graph-based optimization problem that evolves microstructures recognizing confinement and anisotropy constraints. We illustrate the approach by designing optimized morphologies for photovoltaic applications, and evolve an initial morphology into an optimized morphology exhibiting substantially improved short circuit current (68% improvement over a conventional bulk-heterojunction morphology). We show optimized morphologies across a range of thicknesses exhibiting self-similar behavior. Results suggest that thicker films (250 nm) can be used to harvest more incident energy. Our graph based morphogenesis is broadly applicable to microstructure-sensitive design of batteries, biosensors and related applications.

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