Physics-informed machine learning for predicting temperature-dependent chemical properties

Digital Discovery 2026 Open access

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Authors: Mahyar Rajabi Kochi, Hanie Rezaei, Sartaaj Takrim Khan, Bhanu Teja Mamillapalli, Maryam Ebrahimiazar, Haoming Ye, Rose Moosavian, Mohammad Zargartalebi, Dave Sinton, Seyed Mohamad Moosavi
DOI: 10.1039/d5dd00489f
Cited by: 3
References: 66

Abstract

Physics-informed machine learning model decouples chemistry from thermodynamic state, including temperature, to accurately predict fluid properties across thermodynamic conditions. It enables reliable design of new cooling fluids.

Fields of Study & Subjects

Machine Learning in Materials Science Model Reduction and Neural Networks Quantum many-body systems

Publication details

Journal / source
Digital Discovery
Publisher
Royal Society of Chemistry (RSC)
Volume
5
Issue
6
Pages
2491–2506
Publication date
1 January 2026
Language
EN