Machine learning enabled autonomous microstructural characterization in 3D samples
Henry Chan, Mathew Cherukara, Troy D. Loeffler, Badri Narayanan & Subramanian K. R. S. Sankaranarayanan

npj Computational Materials volume 6, Article number: 1 (2020) Cite this article

Abstract
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.

Introduction
Characterization of microstructural and nanoscale features in full 3D samples of materials is emerging to be a key challenge across a range of different technological applications. These microstructural features can range from grain size distribution in metals, voids and porosity in soft materials such as polymers to hierarchical structures and their distributions during self- and directed-assembly processes. It is well known that there is a strong correlation between microstructural/nanoscale features in materials and their observed properties. For the most part, however, grain size characterization is performed on 2D samples and the information from 2D slices is collated to derive the 3D microstructural information, which is inefficient and leads to potential loss of information. As such, a direct 3D classification approach for arbitrary polycrystalline microstructure is crucial and highly desirable, especially given the advancement in 3D characterization techniques such as tomography,1 high energy diffraction microscopy (HEDM),2 and coherent diffraction X-ray imaging.

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