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

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

January 1, 2025Digital DiscoveryOpen Access

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

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Key Points

The aim is to develop a machine learning model to predict chemical properties based on temperature and chemical structure.
Utilized physics-informed machine learning techniques
Analyzed the relationship between chemical structure and thermodynamic properties
Explored temperature dependencies in chemical properties
Predictions closely matched experimental data
Demonstrated improved accuracy over traditional models
Highlighted significant temperature effects on properties

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Implication

Emerging energy systems utilize temperature-dependent chemical properties, suggesting novel prediction techniques.

Abstract

Emerging energy and electronic systems rely on the thermodynamic properties of chemical and cooling fluids. These properties are a function of both chemical structure and temperature. For instance, the dynamic...

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Cite This Study

Kochi et al. (2025) studied this question.

synapsesocial.com/papers/69ccb75916edfba7beb893e2 https://doi.org/10.1039/d5dd00489f

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

Key Points

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Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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