Heat Capacity of Ionic Liquids: Toward Interpretable Chemical Structure-Based Machine Learning Approaches
Bibliographic record
Abstract
This study focuses on predicting the heat capacity of pure liquid-phase ionic liquids (ILs) using machine learning models from various categories, including support vector machines, instance-based learning, ensemble learning, and neural networks, with linear regression serving as a baseline. A key aim of this work is not only to achieve accurate predictions but also to ensure the interpretability of the results, addressing a gap often overlooked in predictive modeling studies. To accomplish this, we curated and cleaned a comprehensive data set of 13,893 data points covering 322 ILs, using temperature and chemical structure-based features as inputs. We evaluated model performance and conducted a thorough interpretability analysis to reveal the patterns of the top-performing model’s predictions, ensuring that they are understandable. All models outperformed the baseline, with XGBoost (eXtreme Gradient Boosting) from the ensemble learning category achieving the best results, with total RMSE, R 2, and AARD (%) values of 11.389, 0.997, and 1.212%, respectively. Shallow neural networks also performed competitively, suggesting that complex deep learning architectures may not be necessary. Both 10-fold and leave-one-IL-out (LOILO) cross-validation further validated the robustness of these results. Importantly, the interpretability analysis identified key factors influencing heat capacity predictions, such as anion size (e.g., NTf 2 and FAP) and alkyl chain length. These factors were validated by testing the model on previously unseen IL examples. Additionally, a user-friendly web application was developed to make predictions, allowing users to input chemical groups or select compounds from a predefined list of 1633 ILs. This study underscores the importance of combining diverse modeling approaches with robust interpretability techniques to achieve reliable and explainable predictions for IL heat capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".