npj: 机器学习—自动表征材料的微结构

来源：知社学术圈

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。

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.