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Physics-informed machine learning for predicting temperature-dependent chemical properties
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DOI：10.1039/D5DD00489F.png)
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Abstract
En 中文
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 viscosity of a fluid can vary by orders of magnitude across the operating range of a cooling system. However; capturing this behavior remains a challenge for experimental and modelling approaches. Machine learning models; although powerful for fixed temperatures; fail to generalize across temperatures due to a lack of data and a lack of embedded physical constraints. Here; we introduce a physics-informed machine learning framework that incorporates established physical relationships; such as the Arrhenius equation or Clausius-Clapeyron; to capture both chemical diversity and temperature dependence. We demonstrate that decoupling chemistry from thermodynamic conditions enables accurate prediction of temperature-dependent dynamic viscosity for both pure compounds and binary mixtures; which we validated with new experimental data. Through a materials-discovery campaign for cooling applications; we show that neglecting temperature effects can cause relative efficiency errors exceeding an order of magnitude; leading to inaccurate materials ranking and suboptimal fluid selection. Finally; we extend the framework to other properties; such as vapor pressure and diffusion coefficient; highlighting a generalizable strategy for accelerating fluid property prediction and design for sustainable technologies.
Keywords:
machine learning
thermodynamic properties
temperature dependence
chemical fluids
materials discovery
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