scholarly journals Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

ACS Catalysis ◽  
2020 ◽  
Vol 10 (16) ◽  
pp. 9438-9444 ◽  
Author(s):  
Nongnuch Artrith ◽  
Zhexi Lin ◽  
Jingguang G. Chen
Keyword(s):  
Machine Learning ◽  
Metal Catalysts ◽  
Ethanol Reforming

Improved Durability of Iron Promoted Non-Precious Metal Catalysts for Hydrogen Generation through Bio-ethanol Reforming

2019 ◽  
Vol 16 (2) ◽  
pp. 523-531
Author(s):  
Lihong Huang ◽  
Jian Xie ◽  
Rongrong Chen ◽  
Deryn Chu ◽  
Andew Hsu
Keyword(s):  
Precious Metal ◽  
Hydrogen Generation ◽  
Metal Catalysts ◽  
Ethanol Reforming ◽  
Precious Metal Catalysts

scholarly journals Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts

2018 ◽  
pp. 45-64 ◽  
Author(s):  
Ichigaku Takigawa ◽  
Ken-ichi Shimizu ◽  
Koji Tsuda ◽  
Satoru Takakusagi
Keyword(s):  
Machine Learning ◽  
Metal Catalysts ◽  
Factors Affecting

Hydrogen formation in ethanol reforming on supported noble metal catalysts

2006 ◽  
Vol 116 (3) ◽  
pp. 367-376 ◽  
Author(s):  
András Erdőhelyi ◽  
János Raskó ◽  
Tamara Kecskés ◽  
Mariann Tóth ◽  
Márta Dömök ◽  
...  
Keyword(s):  
Noble Metal ◽  
Metal Catalysts ◽  
Hydrogen Formation ◽  
Noble Metal Catalysts ◽  
Ethanol Reforming

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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