Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion

F Jirasek, RAS Alves, J Damay… - The journal of …, 2020 - ACS Publications
Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key
property in chemical engineering with relevance to modeling chemical and phase equilibria …

Graph neural networks for the prediction of infinite dilution activity coefficients

EIS Medina, S Linke, M Stoll, K Sundmacher - Digital Discovery, 2022 - pubs.rsc.org
The use of predictive methods for physicochemical properties is of special interest given the
difficulties involved in the experimental determination of large chemical spaces. In this work …

Combining molecular dynamics and machine learning to predict self-solvation free energies and limiting activity coefficients

J Gebhardt, M Kiesel, S Riniker… - Journal of chemical …, 2020 - ACS Publications
Computational prediction of limiting activity coefficients is of great relevance for process
design. For highly nonideal mixtures including molecules with directed interactions, methods …

Prediction of infinite‐dilution activity coefficients with neural collaborative filtering

T Tan, H Cheng, G Chen, Z Song, Z Qi - AIChE Journal, 2022 - Wiley Online Library
Accurate prediction of infinite dilution activity coefficient (γ∞) for phase equilibria and
process design is crucial. In this work, an experimental γ∞ dataset containing 295 solutes …

Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution

EIS Medina, S Linke, M Stoll, K Sundmacher - Digital Discovery, 2023 - pubs.rsc.org
The accurate prediction of physicochemical properties of chemical compounds in mixtures
(such as the activity coefficient at infinite dilution γij∞) is essential for developing novel and …

An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution …

D Wu, Z Zhu, J Zhang, H Wen, S Jin… - Industrial & Engineering …, 2024 - ACS Publications
The infinite dilution activity coefficient (γ∞) is a significant thermodynamic property for phase
equilibrium prediction. Herein, a solute–solvent interactive attention module is proposed to …

[HTML][HTML] Thermodynamically consistent vapor-liquid equilibrium modelling with artificial neural networks

A Carranza-Abaid, HF Svendsen, JP Jakobsen - Fluid Phase Equilibria, 2023 - Elsevier
Abstract An integration of Artificial Neural Networks (ANNs) and thermodynamics through
the application of Neural Network Programming (NNP) is proposed. Thermodynamic …

Influence of thermodynamically consistent data on artificial neural network modeling: Application to NH3 solubility data in room temperature ionic liquids

A Saali, M Shokouhi, MK Salooki, M Esfandyari… - Journal of Molecular …, 2023 - Elsevier
A general thermodynamic consistency of temperature, pressure, and solubility (TPx) data for
binary mixtures including, ammonia+ Room Temperature Ionic Liquids (ILs), has been …

Influence of thermodynamically inconsistent data on modeling the solubilities of refrigerants in ionic liquids using an artificial neural network

EN Fierro, CA Faúndez, AS Muñoz - Journal of Molecular Liquids, 2021 - Elsevier
In this work, a general thermodynamic consistency test is applied to analyze phase
equilibrium data (PTx) for binary refrigerant and ionic liquid mixtures. The Valderrama-Patel …

An artificial neural network-based NRTL model for simulating liquid-liquid equilibria of systems present in biofuels production

HE Reynel-Ávila, A Bonilla-Petriciolet… - Fluid Phase …, 2019 - Elsevier
A new hybrid local composition model was developed for simulating the liquid-liquid
equilibria of systems involved in biofuels production. This model was based on the …