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

CDS

• 批准号: 2311104
• 负责人: Vitaliy Yurkiv
• 金额: $ 49.93万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311104&HistoricalAwards=false
• 关键词: CDS&E; Deep; Learning; Framework; Evaluation

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.

该项目由材料研究部的凝聚态物质与材料理论和金属与金属纳米结构项目共同资助。电子显微镜可以拍下纳米尺寸的金属颗粒的美丽和信息丰富的照片，但是这些图像有时很难解释。激发这项工作的化学复杂金属合金纳米颗粒（CCA-NPs）在催化，能量转换和存储以及生物/等离子体成像等广泛应用中具有很大的兴趣。本研究项目开发了一种以实验测量为支持的多学科建模方法，以跟上原子分辨微观测量日益广泛应用的步伐。本项目的主要目标是利用基于机器学习的新型建模框架，从不同成分和尺寸的CCA-NPs的实验高分辨率电子显微镜图像中提取原子柱高度和化学元素信息。虽然目前的工作主要是由纳米颗粒驱动的，但框架是通用的，并且很容易扩展到适用于扫描透射电子显微镜的其他纳米科学研究，如催化、晶体学和相演化。该团队将在他们的本科和研究生课程中引入与本项目相关的一些主题，强调熟悉当前的研究问题和不同计算方法的直接经验，以及开放源码和商业软件。调查人员及其小组成员通过伊利诺伊大学芝加哥分校（UIC）开放日和UIC青年计划积极参与当地高中女性和代表性不足群体成员的外展活动。带有叙述的计算机模拟结果将用于课堂教学，也将通过微博和社交网络服务向公众和科学界提供。技术摘要机器和深度学习算法的最新发展，加上原位电子显微镜技术的不断进步，为有效分析各种材料铺平了道路。本研究使用基于全卷积神经网络的深度学习模型来解析原子分辨率透射（TEM）和扫描（SEM）电子显微镜图像中CCA-NPs的元素分布。所提出的神经网络的目标是通过语义分割来学习显微镜图像的像素强度与CCA-NP结构原子列中不同组成元素的原子数之间的非线性相关性。尽管迫切需要确定结构和元素分布，但目前的实验工作依赖于试错分析，往往涉及许多假设。这是由于TEM图像的非线性和复杂性，阻止了原子柱高度和元素分布的直接估计。因此，本项目的主要目标是通过实验高分辨率透射电镜（HRTEM）和扫描透射电镜（STEM）图像提供不同尺寸和成分的CCA-NPs，包括高熵合金（HEAs）的信息。提出了一个基于机器学习（ML）、进化方法（EA）和HRTEM和STEM测量支持的密度泛函理论（DFT）计算的集成多学科建模框架。一个辅助目标是提供一系列实验条件，以获得可靠的HRTEM图像。该项目采用多学科建模方法，通过(i)推进用于评估实验获得的图像的深度学习技术的当前状态，（ii）使用Wulff构建为CCA-NPs生成物理上有意义和可靠的训练数据，（iii）使用进化方法评估HEAs中的元素基元，提供了对HRTEM/STEM实验获得的CCA-NPs图像的分析和解释的范式转变。（iv）更深入地了解显微镜参数（如剂量、焦距、散焦等）对神经网络预测质量的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Role of Kinetics and Thermodynamics in Controlling the Crystal Structure of Nickel Nanoparticles Formed on Reduced Graphene Oxide: Implications for Energy Storage and Conversion Applications

• DOI: 10.1021/acsanm.2c05528
• 发表时间: 2023-06
• 期刊: ACS Applied Nano Materials
• 影响因子: 5.9
• 作者: Mahmound Tamadoni Saray;Vitaliy Yurkiv;R. Shahbazian‐Yassar

Enhanced Bacterial Growth by Polyelemental Glycerolate Particles

• DOI: 10.1021/acsabm.2c01052
• 发表时间: 2023-03-18
• 期刊: ACS APPLIED BIO MATERIALS
• 影响因子: 4.7
• 作者: Phakatkar，Abhijit H.;Goncalves，Josue M.;Shahbazian-Yassar，Reza
• 通讯作者: Shahbazian-Yassar，Reza

In Situ Thermolysis of a Ni Salt on Amorphous Carbon and Graphene Oxide Substrates

• DOI: 10.1002/adfm.202213747
• 发表时间: 2023-04
• 期刊: Advanced Functional Materials
• 影响因子: 19
• 作者: Mahmound Tamadoni Saray;Vitaliy Yurkiv;R. Shahbazian‐Yassar

What are the Tower's method products: Metal-hydroxides or metal-glycerolates?

塔的方法产品有哪些：金属氢氧化物或金属甘油盐？

• DOI: 10.1016/j.matchar.2022.112636
• 发表时间: 2023
• 期刊: Materials Characterization
• 影响因子: 4.7
• 作者: Gonçalves, Josué M.;Lima, Irlan S.;Phakatkar, Abhijit H.;Pereira, Rafael S.;Martins, Paulo R.;Araki, Koiti;Angnes, Lúcio;Shahbazian-Yassar, Reza
• 通讯作者: Shahbazian-Yassar, Reza