Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers
| Autor: | Cindy Trinh, Dimitrios Meimaroglou, Sandrine Hoppe |
|---|---|
| Přispěvatelé: | Laboratoire Réactions et Génie des Procédés (LRGP), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
materials design
Computer science Chemical Product Engineering Bioengineering 02 engineering and technology TP1-1185 010402 general chemistry Machine learning computer.software_genre Key issues 01 natural sciences Product engineering Field (computer science) Critical discussion [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Machine Learning [CHIM.GENI]Chemical Sciences/Chemical engineering [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Selection (linguistics) Chemical Engineering (miscellaneous) [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering Complex problems QD1-999 Focus (computing) business.industry Process Chemistry and Technology Chemical technology prediction of chemical reactions 021001 nanoscience & nanotechnology artificial intelligence sensorial analysis [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation 0104 chemical sciences Chemistry data-driven modeling Artificial intelligence State (computer science) 0210 nano-technology business computer |
| Zdroj: | Processes Processes, MDPI, 2021, 9, ⟨10.3390/pr9081456⟩ Processes, Vol 9, Iss 1456, p 1456 (2021) |
| ISSN: | 2227-9717 |
| DOI: | 10.3390/pr9081456⟩ |
| Popis: | International audience; Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the fieldas well as the newcomer. |
| Databáze: | OpenAIRE |
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