Machine learning enabled autonomous microstructural characterization in 3D samples

www.nature.com/npjcompumats ARTICLE OPEN Machine learning enabled autonomous microstructural characterization in 3D samples Henry Chan 1* , Mathew Cherukara1, Troy D. Loeffler1, Badri Narayanan1,2 and Subramanian K. R. S. Sankaranarayanan1,3* 1234567890():,; 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. npj Computational Materials (2020)6:1 ; https://doi.org/10.1038/s41524-019-0267-z 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. Most industry relevant structural materials are polycrystalline in nature, and often contain thousands or millions of grains. Within each grain, the lattice arrangement of atoms is nearly identical, but the atomic orientations are different for each adjoining grain. Grain boundaries are interfaces where two grains or crystallites having different orientations meet without a disruption in the continuity of the material. Note that the thermodynamic equilibrium state of these polycrystalline materials is single crystal.3 It is, however, well known that materials are often arrested or trapped in local minima, i.e., in the polycrystalline state. Grain formation in polycrystalline films during their growth and processing is a complex process and is highly sensitive to several parameters such as temperature, deposition rate, dopant concentration, pressure, and impurity concentration to name a few. Nuclei when formed are nanoscopic – critical sizes start from tens of atoms – and lead to nanocrystalline solids that subsequently consolidate into larger grains. These ubiquitous phenomena, from “rare events” such as nucleation to the subsequent phase transformation in crystalline solids, lie at the heart of a spectrum of physico-chemical processes that govern nanoscale material transformation. They have been a fundamental problem in materials science and are also relevant to a broad range of energy applications. Average grain size and grain distribution are critical microstructural features that impact several physical, mechanical, optical, chemical, and thermal properties to name a few, and represent fundamental quantities to characterize polycrystalline materials.4–9 For example, the Hall–Petch relationship10,11 states that the final average grain size after the transformation is directly related to the strength, hardness, stress–strain properties and fatigue of a material. Several previous investigations have shown that grain size distribution has a significant effect on mechanical properties. For example, Berbenni et al.12 showed that for a given average grain size, broadening of the grain size dispersion reduces the strength of a material. The classification and quantification of polycrystalline microstructure is therefore critically important in predicting material responses. A microstructural understanding is also important for the design and discovery of new materials with tailored properties, such as stronger materials that minimize fatigue failures of a machine component during their operation lifetime. The ubiquitous connection between microstructure (mainly, grain-size distribution) of a material and its physical properties has motivated numerous studies on developing robust techniques to analyze microscopy/tomography images.13–18 ASTM outlines the industry standard for grain identification in 2D data,16 which consists of methods such as matching, planimetric, and intercept 1 Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, USA. 2Department of Mechanical Engineering, University of Louisville, Louisville, KY, USA. Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA. *email: ; 3 Published in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences H. Chan et al. 1234567890():,; 2 methods. These methods, albeit can achieve high accuracy (±0.25 grain size units) and reproducibility, can be severely impaired when the intersection criterion (for distinguishing grains) is poorly chosen or the grain-size distribution is non-uniform.16 In addition, these technique often require tedious manual measurements, and automation is challenging due variability in etching level or contrast differences although electron back scattering diffraction methods have been recently proposed to eliminate subjectivity surrounding existence/location of grain boundaries.15,19 Automated methods for grain identification in 2D data have been developed over the years. For example, there are supervised convolutional neural network (CN (...truncated)