CDS&E: A Deep Learning Framework for Evaluation of Electron Microscopy Images of Chemically-Complex Metallic Materials

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This project is co-funded by the Condensed-Matter-and-Materials-Theory and Metals-and-Metallic-Nanostructures programs in the Division of Materials Research.Nontechnical summaryElectron microscopes take beautiful and informative pictures of metal particles of nanometer size, but the images can sometimes be difficult to interpret. The chemically-complex metallic-alloy nanoparticles (CCA-NPs) that motivate this work are of great interest in a wide range of applications including catalysis, energy conversion and storage, and bio/plasmonic imaging. This research project develops a multi-disciplinary modeling methodology supported by experimental measurements to keep pace with the growing widespread application of atomically resolved microscopic measurements. The main objective of this project is to utilize a novel modeling framework based on machine learning to extract information about atomic column heights and chemical elements from experimental high-resolution electron microscopy images of CCA-NPs of different compositions and sizes. Although the present work is motivated primarily by nanoparticles, the framework is general and easily extendable to other nanoscience research amenable to scanning transmission electron microscopy, such as catalysis, crystallography, and phase evolution.The team will introduce in their undergraduate and graduate courses a number of topics related to the present project, stressing familiarity with current research problems and direct experience with different computational methods as well as open source and commercial software. The investigators and their group members actively participate in outreach activities for local high-school women and members of underrepresented groups through the University of Illinois Chicago (UIC) Open House and the UIC Youth Program. The computer simulation results with narratives will be used for classroom teaching and will also be made available to the public and scientific community via microblogging and social-network services.Technical summaryThe latest developments in machine and deep-learning algorithms, coupled with continuing progress of in-situ electron-microscopy techniques, have paved the way to an effective analysis of a variety of materials. This research uses a deep-learning model built on a fully convolutional neural network to resolve the elemental distribution of CCA-NPs represented in atomic-resolution transmission (TEM) and scanning (SEM) electron microscopy images. The objective of the proposed neural network is to learn, through semantic segmentation, the non-linear correlations between the pixel intensities of microscopy images and the number of atoms of different constituent elements in the atomic columns of CCA-NP structures. In spite of the critical need in determining structures and elemental distributions, current experimental efforts rely on trial-and-error analysis and often involve many assumptions. This is due to the nonlinearity and complexity of TEM images, preventing the straightforward estimation of atomic column heights and elemental distributions. Thus, the main objective of this project is to provide such information from experimental high-resolution TEM (HRTEM) and scanning TEM (STEM) images of CCA-NPs of different sizes and compositions, including high-entropy alloys (HEAs). An integrated, multi-disciplinary modeling framework based on machine learning (ML), an evolutionary approach (EA), and density-functional-theory (DFT) calculations supported by HRTEM and STEM measurements is proposed. A supporting objective is to provide a range of experimental conditions for which reliable HRTEM images may be acquired. This project provides a paradigm shift in the analysis and interpretation of HRTEM/STEM experimentally acquired images of CCA-NPs employing a multi-disciplinary modeling approach through (i) advancing the current state of the art of deep-learning techniques for evaluation of experimentally obtained images, (ii) generation of physically meaningful and reliable training data for CCA-NPs using the Wulff Construction, (iii) evaluation of elemental motifs in HEAs using an evolutionary approach, and (iv) more profound understanding of the influence of the microscope parameters (e.g., dose, focal spread, defocus, etc.) on the quality of neural-network predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.