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Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Hayer, Nicolas, Wendel, Thorsten, Mandt, Stephan, Hasse, Hans, Jirasek, Fabian
Format:	Preprint
Publié:	2024
Sujets:	Chemical Physics Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

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