Thermodynamics-informed machine learning for predicting temperature-dependent chemical properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 thermodynamics-informed machine learning framework that incorporates established physical relationships, such as the Arrhenius equation, to capture both chemical diversity and temperature dependence. We show that decoupling chemistry from thermodynamic conditions enables accurate prediction of temperature-dependent dynamic viscosity, which we validated experimentally. Through a materials-discovery campaign for cooling applications, we show that neglecting temperature effects can cause over 90% errors in performance evaluation, 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it