材料介觀尺度的微結構，如金屬中的晶粒、聚合物中的孔隙以及軟物質中的分級結構等，其尺寸及分佈特徵對於材料的力學、物理及化學等性能具有重要影響。表徵材料微結構對於相關的技術應用具有重要意義。然而，如何在三維材料樣品中實現快速、準確和自動化的微結構表徵仍是當前面臨的重要挑戰。
來自美國阿貢實驗室和伊利諾伊大學芝加哥分校的Sankaranarayanan教授團隊結合無監督機器學習、拓撲分類和圖像處理技術建立了一個微結構分析方案，可以自動識別並分析三維數據樣品中的微結構。這些數據樣品既可以來自分子動力學的模擬結果，也可是實驗的測量結果。該方案首先通過拓撲分類識別樣品中的不同微結構，之後利用聚類算法對微結構進行分類並分析，最後通過精修進一步得到準確的微結構特徵。為證明方法的有效性，作者利用金屬、聚合物和複雜流體等五個體系的模擬及實驗表徵數據開展了比較研究。結果表明上述方法不僅準確、計算效率高，並且對體系中的缺陷不敏感。該方法有望應用於光源等大型表徵設備上，用於實時表徵影響材料性能的複雜微結構。
該文近期發表於npj Computational Materials 6: 1 (2020)，英文標題與摘要如下，點擊https://www.nature.com/articles/s41524-019-0267-z可以自由獲取論文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.
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