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Machine Learning: Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets (Small Methods 5/2021)
Author(s) -
Blum Thomas,
Graves Jeffery,
Zachman Michael J.,
PoloGarzon Felipe,
Wu Zili,
Kannan Ramakrishnan,
Pan Xiaoqing,
Chi Miaofang
Publication year - 2021
Publication title -
small methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.66
H-Index - 46
ISSN - 2366-9608
DOI - 10.1002/smtd.202170020
Subject(s) - artificial intelligence , computer science , machine learning , unsupervised learning , trace (psycholinguistics) , philosophy , linguistics
In article number 2100035, Ramakrishnan Kannan, Xiaoqing Pan, Miaofang Chi, and co‐workers developed a robust, unsupervised machine learning data analysis method to reveal encapsulation of metal catalysts that are otherwise overlooked in microscopy datasets. Therefore, providing a reliable tool for analyzing the nano‐environment of catalysts and being generally applicable to any spectroscopic analysis where revealing a trace signal is challenging.