Abstract
The availability of temperature-dependent physicochemical property data forms the cornerstone of process simulation, optimization, and sustainable molecular and product design. However, a critical data gap persists, as experimental measurements are accessible for only a small subset of known chemicals. This renders experimental characterization resource-prohibitive, often compelling reliance on empirical estimation methods. Moreover, although many models offer single-point predictions at fixed temperatures, accurately modeling continuous temperature-dependent behavior remains challenging. Conventional methods frequently overlook intermediate variations, resulting in limited extrapolation capability. To overcome these limitations, we introduce a mechanism-guided hybrid modeling framework that integrates physical insights into data-driven models. This framework is built on two strategies. Strategy ? targets trend correction by generating a continuous representation from discrete single-point predictions, incorporating descriptors and slopes. Strategy ? addresses bias removal by anchoring a baseline to a high-accuracy point estimate and fitting the remaining deviations. The framework's effectiveness is evidenced by evaluations across ten thermophysical properties: Strategy ? achieves MSE reductions of 19.23% and 20.33% for the quantitative structure-property relationship and group contribution methods, respectively. Strategy ? provides a more substantial improvement, attaining an 81.63% MSE reduction for the gradient boosting decision tree regression model. This work demonstrates that incorporating trend and slope constraints facilitates physically consistent, bias-corrected, and accurate predictions, offering a scalable approach to bridge the data gap and accelerate computer-aided engineering and design.