Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The use of machine learning (ML) in catalysis has been significantly increased in recent years due to the astonishing developments in data processing technologies and the accumulation of a large amount of data in published literature and databases. The data generated in house or extracted from external sources have been analyzed using various ML techniques to see patterns, develop models for prediction and deduce heuristic rules for the future. This communication aims to review the works involving knowledge discovery in catalysis using ML techniques; the basic principles, common tools and implementation of ML in catalysis are also summarized.
Açıklama
Anahtar Kelimeler
Machine Learning, Data Mining, Knowledge Extraction, Meta-Analysis, Catalysis, Artificial Neural-Network, Selective Co Oxidation, Metal-Organic Frameworks, Decision Tree Analysis, Gas Shift Reaction, High-Performance, Statistical-Analysis, Ni/Al2o3 Catalysts, Past Publications, Methane
Kaynak
Catalysis Reviews-Science and Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
63
Sayı
1
