Accuracy of computational solvation free energies for neutral and ionic compounds: Dependence on level of theory and solvent model
Bibliographic record
Abstract
Abstract Gas to aqueous phase standard state (1 atm to 1 mol/L; 298.15 K) free energies of solvation ([DELTA]G^o^~solv~) were calculated for a range of neutral and ionic inorganic and organic compounds using various levels and combinations of Hartree-Fock and density functional theory (DFT) and composite methods (CBS-Q//B3, G4MP2, and G4) with the IEFPCM-UFF, CPCM, and SMD solvation models in Gaussian 09 (G09). For a subset of highly polar and generally polyfunctional neutral organic compounds previously identified as problematic for prior solvation models, we find significantly reduced [DELTA]G^o^~solv~ errors using the revised solvent models in G09. The use of composite methods for these compounds also substantially reduces their apparent [DELTA]G^o^~solv~ errors. In contrast, no general level of theory effects between the B3LYP/6-31+G** and G4 methods were observed on a suite of simpler neutral, anionic, and cationic molecules commonly used to benchmark solvation models. Further investigations on mono- and polyhalogenated short chain alkanes and alkenes and other possibly difficult functional groups also revealed significant [DELTA]G^o^~solv~ error reductions by increasing the level of theory from DFT to G4. Future solvent model benchmarking efforts should include high level composite method calculations to allow better discrimination of potential error sources between the levels of theory and the solvation models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".