Cover Image

Differentiable Thermodynamic Phase-Equilibria for Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hicham, Karim K. Ben, Ascani, Moreno, Rittig, Jan G., Mitsos, Alexander
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)

Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
by: Rittig, Jan G., et al.
Published: (2022)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)

Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)

Graph Machine Learning for Design of High-Octane Fuels
by: Rittig, Jan G., et al.
Published: (2022)

Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)

Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Federated Learning from Molecules to Processes: A Perspective
by: Rittig, Jan G., et al.
Published: (2025)

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
by: Schweidtmann, Artur M., et al.
Published: (2022)

GraphXForm: Graph transformer for computer-aided molecular design
by: Pirnay, Jonathan, et al.
Published: (2024)

Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
by: Almanasreh, Marah, et al.
Published: (2026)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
by: Tan, Tai Xuan, et al.
Published: (2026)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
by: Mayfrank, Daniel, et al.
Published: (2023)

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
by: Schulze, Jan C., et al.
Published: (2025)

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
by: Loftin, Robert, et al.
Published: (2023)

Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling
by: Bosch, Robbert, et al.
Published: (2025)

Sample-Efficient Reinforcement Learning of Koopman eNMPC
by: Mayfrank, Daniel, et al.
Published: (2025)

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
by: Fuchs, Paul, et al.
Published: (2025)

Data-Driven Conditional Flexibility Index
by: Wedemeyer, Moritz, et al.
Published: (2026)

A Systematic Evaluation of Molecular Mixture Behavior Prediction
by: Leenhouts, Roel J., et al.
Published: (2026)

End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
by: Mayfrank, Daniel, et al.
Published: (2025)

Machine Learning for Improved Density Functional Theory Thermodynamics
by: Simak, Sergei I., et al.
Published: (2025)

Machine Learning: Progress and Prospects
by: Gammerman, Alexander
Published: (2025)

Learning Equilibria in Matching Games with Bandit Feedback
by: Athanasopoulos, Andreas, et al.
Published: (2025)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
by: Mayfrank, Daniel, et al.
Published: (2024)

Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
by: Koronaki, Eleni D., et al.
Published: (2024)

Bayesian Optimization of Partially Known Systems using Hybrid Models
by: Cramer, Eike, et al.
Published: (2026)

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
by: Majewski, Maciej, et al.
Published: (2022)

A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters
by: Dey, Debsundar, et al.
Published: (2023)

Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
by: Velioglu, Mehmet, et al.
Published: (2026)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)

Differential Machine Learning for Time Series Prediction
by: Yadav, Akash, et al.
Published: (2025)

Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning
by: Freihaut, Till, et al.
Published: (2025)

Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
by: Liu, Juejing, et al.
Published: (2025)

Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning
by: Chew, Robert, et al.
Published: (2025)