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Automating electron microscopy: machine learning for cluster identification

自动化电子显微镜：用于簇识别的机器学习

基本信息

批准号：
EP/V029797/1
负责人：
Thomas Slater
金额：
$ 23.37万
依托单位：
Diamond Light Source
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV029797%2F1
关键词：
Automating electron microscopy machine learning

项目摘要

Nanoparticles are of particular interest in the field of catalysis because of the high proportion of their atoms that are available at their surface (a high surface-to-volume ratio). Catalysis happens only at the surface of materials and is controlled by the electronic and spatial configuration of surface atoms. Nanoparticles of certain sizes are known to take up a small number of fixed shapes that possess well known configurations of atoms at their surface. Determining the shape of nanoparticles is a difficult question that requires very high-resolution characterisation techniques, such as electron microscopy.In this project, a convolutional neural network will be trained to recognize the different shapes of small nanoparticles. A convolutional neural network is a type of machine learning algorithm that can be trained to recognize image features. Once trained, the neural network will be used to determine the proportion of different particle shapes found in platinum nanoparticles of different sizes. This will determine which particle shapes have the lowest potential energy for each size and therefore will guide scientists to know which particles are likely to act as better catalysts for chemical reactions and processes.The trained neural network will be made available for anyone to use via its incorporation in to open-source software. This will allow anyone with electron microscope images of nanoparticles to use the same technique to analyse the shape of small nanoparticles.

纳米颗粒在催化领域中特别令人感兴趣，因为它们的原子在其表面上的比例很高（高的表面与体积比）。催化作用只发生在材料的表面，并受表面原子的电子和空间构型控制。已知某些尺寸的纳米颗粒具有少量的固定形状，这些形状在其表面具有众所周知的原子构型。确定纳米颗粒的形状是一个难题，需要非常高分辨率的表征技术，例如电子显微镜。在这个项目中，将训练卷积神经网络来识别小纳米颗粒的不同形状。卷积神经网络是一种机器学习算法，可以训练它来识别图像特征。一旦经过训练，神经网络将用于确定不同尺寸的铂纳米颗粒中不同颗粒形状的比例。这将确定哪些颗粒形状具有最低的势能，从而指导科学家了解哪些颗粒可能作为化学反应和过程的更好催化剂。经过训练的神经网络将通过纳入开源软件供任何人使用。这将允许任何拥有纳米粒子电子显微镜图像的人使用相同的技术来分析小纳米粒子的形状。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

无机纳米颗粒的透射电子显微镜图像的可训练分割。

DOI：
10.1111/jmi.13110
发表时间：
2022-12
期刊：
JOURNAL OF MICROSCOPY
影响因子：
2
作者：
Bell, Cameron G.;Treder, Kevin P.;Kim, Judy S.;Schuster, Manfred E.;Kirkland, Angus, I;Slater, Thomas J. A.
通讯作者：
Slater, Thomas J. A.

Methanol synthesis from CO2 and H2 using supported Pd alloy catalysts.

使用负载型 Pd 合金催化剂从 CO2 和 H2 合成甲醇。