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Front Cover: Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data (ChemCatChem 18/2019)
Author(s) -
Suzuki Keisuke,
Toyao Takashi,
Maeno Zen,
Takakusagi Satoru,
Shimizu Kenichi,
Takigawa Ichigaku
Publication year - 2019
Publication title -
chemcatchem
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 1.497
H-Index - 106
eISSN - 1867-3899
pISSN - 1867-3880
DOI - 10.1002/cctc.201901455
Subject(s) - front cover , catalysis , computer science , cover (algebra) , front and back ends , artificial intelligence , nanotechnology , chemistry , machine learning , data science , biochemical engineering , materials science , engineering , organic chemistry , mechanical engineering , operating system
The Front Cover shows future catalysis research using artificial intelligence (AI). The discovery and development of novel catalysts are essential components of the transition to a sustainable future. Recent revolutions made in data science could have a significant impact on catalysis research, and it could accelerate the design of new catalysts. In their Full Paper, Ken‐ichi Shimizu, Ichigaku Takigawa and co‐workers propose a new machine learning (ML) approach that considers elemental features as input representations instead of inputting catalyst compositions directly. This ML method has the potential for catalyst discovery, including catalytic reactions with limited catalyst composition overlap in the available data. Although the attention of many researches has focused on building ML models from computationally derived (well‐behaved) datasets, developing methods to use ML with “real world” experimental data would be more useful. The presented ML model would serve as an effective technique to deal with experimentally produced data. More information can be found in the Full Paper by Keisuke Suzuki et al. on page 4537 in Issue 8, 2019 (DOI: 10.1002/cctc.201900971).