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

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 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.