Zobrazeno 1 - 7 of 7 pro vyhledávání: '"Cindy Trinh"'
1
Akademický článek
A Comprehensive Study on the Styrene–GTR Radical Graft Polymerization: Combination of an Experimental Approach, on Different Scales, with Machine Learning Modeling
Autor: Cindy Trinh, Sandrine Hoppe, Richard Lainé, Dimitrios Meimaroglou
Publikováno v: Macromol, Vol 3, Iss 1, Pp 79-107 (2023)
The study of the styrene–Ground Tire Rubber (GTR) graft radical polymerization is particularly challenging due to the complexity of the underlying kinetic mechanisms and nature of GTR. In this work, an experimental study on two scales (∼10 mL and
Externí odkaz: https://doaj.org/article/19b24b6589834a08a0dd1f080958ef9e
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2
Akademický článek
On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 2—Applicability Domain and Outliers
Autor: Cindy Trinh, Silvia Lasala, Olivier Herbinet, Dimitrios Meimaroglou
Publikováno v: Algorithms, Vol 16, Iss 12, p 573 (2023)
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it desc
Externí odkaz: https://doaj.org/article/a214218bc4b1403d85876d1e645c33b6
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4
Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers
Autor: Cindy Trinh, Dimitrios Meimaroglou, Sandrine Hoppe
Publikováno v: Processes
Processes, MDPI, 2021, 9, ⟨10.3390/pr9081456⟩
Processes, Vol 9, Iss 1456, p 1456 (2021)
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
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5684ec37c3e92ccbabd158b130fde30
https://hal.univ-lorraine.fr/hal-03367703
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