nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems

Treder, Kevin P., Huang, Chen, Bell, Cameron G., Slater, Thomas J. A. ORCID: https://orcid.org/0000-0003-0372-1551, Schuster, Manfred E., Özkaya, Doğan, Kim, Judy S. and Kirkland, Angus I. 2023. nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems. npj Computational Materials 9 (1) , 18. 10.1038/s41524-022-00949-7

	PDF - Published Version Available under License Creative Commons Attribution. Download (2MB)
	PDF - Supplemental Material Available under License Creative Commons Attribution. Download (809kB)

Abstract

We describe nNPipe for the automated analysis of morphologically diverse catalyst materials. Automated imaging routines and direct-electron detectors have enabled the collection of large data stacks over a wide range of sample positions at high temporal resolution. Simultaneously, traditional image analysis approaches are slow and hence unsuitable for large data stacks and consequently, researchers have progressively turned towards machine learning and deep learning approaches. Previous studies often detail work on morphologically uniform material systems with clearly discernible features, limited workable image sizes and training data that may be biased due to manual labelling. The nNPipe data-processing method consists of two standalone convolutional neural networks that were exclusively trained on multislice image simulations and enables fast analysis of 2048 × 2048 pixel images. Inference performance compared between idealised and real industrial catalytic samples and insights derived from subsequent data analysis are placed into the context of an automated imaging scenario.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Cardiff Catalysis Institute (CCI) Chemistry
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	Nature Research
Funders:	EPSRC and Johnson Matthey plc for an iCASE award, EPSRC Grant number EP/S001999/1, Rosalind Franklin Institute and EPSRC Grant Number EP/T033452/1
Date of First Compliant Deposit:	6 February 2023
Date of Acceptance:	10 December 2022
Last Modified:	09 Oct 2023 20:44
URI:	https://orca.cardiff.ac.uk/id/eprint/156534

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item