A Machine Learning Approach for Predicting the Pure-Component Surface Tension of Atmospherically Relevant Organic Compounds
Bibliographic record
Abstract
Atmospheric aerosols are complex mixtures of highly functionalized organic compounds, water, inorganic electrolytes, metals, and carbonaceous species. The surface properties of atmospheric aerosol particles can influence several of their chemical and physical impacts, including their hygroscopic growth, aerosol–cloud interactions, and heterogeneous chemical reactions. The effects of the various compounds within a particle on its surface tension depend in part on the pure-component surface tensions. For many of the myriad of organic compounds of interest, experimental pure-component surface tension data at tropospheric temperatures are lacking, thus, requiring the development and application of property estimation methods. In this work, a compiled database of experimental pure-component surface tension data, covering a wide range of organic compound classes and temperatures, is used to train four different types of machine learning models to predict the temperature-dependent pure-component surface tensions of atmospherically relevant organic compounds. The trained models process input information about the temperature and the molecular structure of an organic compound, initially in the form of a Simplified Molecular Input Line Entry System (SMILES) string, to enable predictions. Our quantitative model assessment shows that extreme gradient-boosted descent along with Molecular ACCess System (MACCS) key descriptors of molecular structure provided the best balance of derived input complexity and model performance, resulting in a root-mean-square error (RMSE) of ∼1 mJ m –2 in pure-component surface tension. Additionally, a simplified model based on molar mass, elemental ratios, and temperature as inputs was developed for use in applications for which molecular structure information is incomplete (RMSE of ∼2 mJ m –2 ). We demonstrate that including predicted pure-component surface tension values in thermodynamically rigorous bulk–surface partitioning calculations may substantially modify the critical supersaturations necessary for aerosol activation into cloud droplets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".