Gibbs–Duhem-informed neural networks for binary activity coefficient prediction-Reference-Cited by-同舟云学术

Gibbs–Duhem-informed neural networks for binary activity coefficient prediction

Published:2023 Issue:6 Volume:2 Page:1752-1767
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Rittig Jan G.¹^ORCID,Felton Kobi C.²^ORCID,Lapkin Alexei A.²^ORCID,Mitsos Alexander¹³⁴^ORCID

Affiliation:

1. Process Systems Engineering (AVT.SVT), RWTH Aachen University, Forckenbeckstraße 51, 52074 Aachen, Germany

2. Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Dr, Cambridge CB3 0AS, UK

3. JARA-ENERGY, Templergraben 55, 52056 Aachen, Germany

4. Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

Abstract

Gibbs–Duhem-informed neural networks provide a flexible hybrid approach to predicting binary activity coefficients with both high accuracy and thermodynamic consistency.

Funder

Deutsche Forschungsgemeinschaft

BASF Corporation

RWTH Aachen University

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00103B

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3. Machine learning for predicting the viscosity of binary liquid mixtures

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