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

arXiv: https://arxiv.org/abs/2408.05220 · HTML: https://mlsanity.com/paper/2408.05220/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion](https://mlsanity.com/paper/2001.10675/) (2020)
- [Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties](https://mlsanity.com/paper/2202.08804/) (2022)

## Built on by (most cited first, 7 of 7)

- [GRAPPA -- A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures](https://mlsanity.com/paper/2501.08729/) (2025)
- [Thermodynamically consistent machine learning model for excess Gibbs energy](https://mlsanity.com/paper/2509.06484/) (2025)
- [Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction](https://mlsanity.com/paper/2502.18998/) (2025)
- [Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods](https://mlsanity.com/paper/2510.18075/) (2025)
- [Prediction of Diffusion Coefficients in Mixtures with Tensor Completion](https://mlsanity.com/paper/2602.23142/) (2026)
- [MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning](https://mlsanity.com/paper/2504.05970/) (2025)
- [Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning](https://mlsanity.com/paper/2607.19114/) (2026)

## Abstract (arXiv)

Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods are widely employed for this purpose but suffer from historically grown, incomplete parameterizations, limiting their applicability and accuracy. In this work, we overcome these limitations by combining GC with matrix completion methods (MCM) from machine learning. We use the novel approach to predict a complete set of pair-interaction parameters for the most successful GC method: UNIFAC, the workhorse for predicting activity coefficients in liquid mixtures. The resulting new method, UNIFAC 2.0, is trained and validated on more than 224,000 experimental data points, showcasing significantly enhanced prediction accuracy (e.g., nearly halving the mean squared error) and increased scope by eliminating gaps in the original model's parameter table. Moreover, the generic nature of the approach facilitates updating the method with new data or tailoring it to specific applications.