材料介观尺度的微结构，如金属中的晶粒、聚合物中的孔隙以及软物质中的分级结构等，其尺寸及分布特征对于材料的力学、物理及化学等性能具有重要影响。表征材料微结构对于相关的技术应用具有重要意义。然而，如何在三维材料样品中实现快速、准确和自动化的微结构表征仍是当前面临的重要挑战。
来自美国阿贡实验室和伊利诺伊大学芝加哥分校的Sankaranarayanan教授团队结合无监督机器学习、拓扑分类和图像处理技术建立了一个微结构分析方案，可以自动识别并分析三维数据样品中的微结构。这些数据样品既可以来自分子动力学的模拟结果，也可是实验的测量结果。该方案首先通过拓扑分类识别样品中的不同微结构，之后利用聚类算法对微结构进行分类并分析，最后通过精修进一步得到准确的微结构特征。为证明方法的有效性，作者利用金属、聚合物和复杂流体等五个体系的模拟及实验表征数据开展了比较研究。结果表明上述方法不仅准确、计算效率高，并且对体系中的缺陷不敏感。该方法有望应用于光源等大型表征设备上，用于实时表征影响材料性能的复杂微结构。
该文近期发表于npj Computational Materials 6: 1 (2020)，英文标题与摘要如下，点击https://www.nature.com/articles/s41524-019-0267-z可以自由获取论文PDF。
Machine learning enabled autonomous microstructural characterization in 3D samples
Henry Chan, Mathew Cherukara, Troy D. Loeffler, Badri Narayanan and Subramanian K. R. S. Sankaranarayanan,
We introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology classification, image processing, and clustering algorithms, and can handle a wide range of microstructure types including grains in polycrystalline materials, voids in porous systems, and structures from self/directed assembly in soft-matter complex solutions. Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples, characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids. To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally published characterization data. Our technique is computationally efficient and provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed material behavior.