Art

J-GLOBAL ID：202302264967547500   Reference number：23A0792660

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

化学製品工学における機械学習:最新技術と新人のためのガイド【JST・京大機械翻訳】

Author (3)： Trinh Cindy ,  Meimaroglou Dimitrios ,  Hoppe Sandrine
Material： Processes (Web)
Volume： 9  Issue： 8  Page： 1456  Publication year： 2021 
JST Material Number： U7264A  ISSN： 2227-9717  Document type： Article
Article type： 文献レビュー  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： Chemical Product Engineering (...

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Thesaurus term： engineering ,  chemical engineering ,  chemical reaction ,  automatic language processing ,  modeling ,  artificial intelligence ,  computer vision ,  property ,  *machine learning
Semi thesaurus term： 複雑性 ,  データ駆動 ,  *化学製品 , 【Automatic Indexing@JST】

Author keywords (7)： machine learning ,  artificial intelligence ,  Chemical Product Engineering ,  data-driven modeling ,  materials design ,  sensorial analysis ,  prediction of chemical reactions

JST classification (1)： Artificial intelligence  (JE08000Z)

Reference (196)：

• Mitchell, T. Machine Learning; McGraw-Hill: New York, NY, USA, 1997.
• Butler, K.T.; Davies, D.W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018, 559, 547-555.
• Elton, D.C.; Boukouvalas, Z.; Fuge, M.D.; Chung, P.W. Deep learning for molecular design-A review of the state of the art. Mol. Syst. Des. Eng. 2019, 4, 828-849.
• Pilania, G. Machine learning in materials science: From explainable predictions to autonomous design. Comput. Mater. Sci. 2021, 193, 110360.
• Zhou, T.; Song, Z.; Sundmacher, K. Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design. Engineering 2019, 5, 1017-1026.

Terms in the title (4)： 化学製品 ,  工学 ,  機械学習 ,  新人