Machine Learning: Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets (Small Methods 5/2021) | Zendy

Blum Thomas | Zendy; Graves Jeffery | Zendy; Zachman Michael J. | Zendy; PoloGarzon Felipe | Zendy; Wu Zili | Zendy; Kannan Ramakrishnan | Zendy; Pan Xiaoqing | Zendy; Chi Miaofang | Zendy

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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.

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