High Throughput Discovery of Lightweight Corrosion-Resistant Compositionally Complex Alloys

Compositionally complex alloys hold the promise of simultaneously attaining superior combinations of properties such as corrosion resistance, light-weighting, and strength. Achieving this goal is a challenge due in part to a large number of possible compositions and structures in the vast alloy design space. High throughput methods offer a path forward, but a strong connection between the synthesis of a given composition and structure with its properties has not been fully realized to date. Here we present the rapid identification of light weight highly corrosion-resistant alloys based on combinations of Al and Cr in a Cantor-like base alloy (Al-Co-Cr-Fe-Ni). Previously unstudied alloy stoichiometries were identified using a combination of high throughput experimental screening coupled with key metallurgical and electrochemical corrosion tests, identifying alloys with excellent passivation behavior. Importantly, the electrochemical impedance modulus of the exposure-modified, air-formed film at the corrosion potential was found as an accurate non-destructive predictor of corrosion and passivation characteristics. Multi-element EXAFS analyses connected more ordered type chemical short range order in the Ni-Al 1st nn shell to poorer corrosion. This report underscores the utility of high throughput exploration of compositionally complex alloys for the identification and rapid screening of vast stoichiometric space

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.Comment: Accepted with minor revisions in npj Computational Materials, Presented in NIPS 2018 Workshop: Machine Learning for Molecules and Material

Self-driving Multimodal Studies at User Facilities

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022). AI4Mat Worksho

Microstructure Representations: Applied Computer Vision Methods for Microstructure Characterization

<p>Recent advances in computing power and automated microstructural image acquisition have opened the doors to data-driven quantitative microstructure analysis. Extraction of salient microstructure features is a crucial enabling component in this rapidly developing field of research; in the past decade the computer vision community has made enormous progress in this area, much of which has gone relatively unexplored by the quantitative microstructure analysis community. This dissertation explores applications of image texture recognition algorithms to engineer efficiently computable generic microstructure descriptors, enabling quantitative microstructure comparisons between and across a wide variety of materials systems. The literature review serves as a broad, high-level introduction for the materials scientist to some of the major themes in image recognition, along with some brief discussion of their relationship to contemporary microstructure science. After establishing that these image texture recognition algorithms can be effectively applied to classify diverse microstructure datasets, I begin to explore novel materials science applications. These include characterization and qualification of powder materials, exploratory analysis of large microstructure datasets, and extraction of quantitative relationships between materials processing and properties metadata and microstructural image features. The fusion of microstructure image analysis and contemporary machine vision techniques will facilitate development of robust autonomous microscopy systems, and may support quantitative engineering standards for complex hierarchical microstructure systems.</p

A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures

This data article presents a data set comprised of 2048 synthetic scanning electron microscope (SEM) images of powder materials and descriptions of the corresponding 3D structures that they represent. These images were created using open source rendering software, and the generating scripts are included with the data set. Eight particle size distributions are represented with 256 independent images from each. The particle size distributions are relatively similar to each other, so that the dataset offers a useful benchmark to assess the fidelity of image analysis techniques. The characteristics of the PSDs and the resulting images are described and analyzed in more detail in the research article “Characterizing powder materials using keypoint-based computer vision methods” (B.L. DeCost, E.A. Holm, 2016) [1]. These data are freely available in a Mendeley Data archive “A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures” (B.L. DeCost, E.A. Holm, 2016) located at http://dx.doi.org/10.17632/tj4syyj9mr.1 [2] for any academic, educational, or research purposes

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

© 2019, The Author(s). X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5 min or less