QM/MM molecular dynamics simulations of the hydration of Mg(II) and Zn(II) ions
Bibliographic record
Abstract
The hydration of Mg2+ and Zn2+ is examined using molecular dynamics simulations using 3 computational approaches of increasing complexity: the CHARMM nonpolarizable force field based on the TIP3P water model, the Drude polarizable force field based on the SWM4-NDP water model, and a combined QM/MM approach in which the inner coordination sphere is represented using a high-quality density functional theory (DFT) model (PBE/def2-TZVPP), and the remainder of the bulk water solvent is represented using the polarizable SWM4-NDP water model. The characteristic structural distribution functions (radial, angular, and tilt) are comparedand show very good agreement between the polarizable force field and QM/MM approaches. They predict an average Mg–O distance of 2.11 Å and an Zn–O distance of 2.13 Å, in good agreement with the available experimental neutron scattering and EXAFS data, while the Mg–O distances calculated using the nonpolarizable force field are 0.1 Å too short. Mg2+ (aq) and Zn2+ (aq) both have a coordination number of 6 and have a remarkably similar octahedral coordination mode, despite the chemical differences between these ions. Thermodynamic integration was used to calculate the relative hydration free energies of these ions (ΔΔGhydr). The nonpolarizable model is in error by 60 kcal mol– 1 and incorrectly predicts that Mg2+ has the more negative hydration energy. The Drude polarizable model predicts a ΔΔGhydr of only –13.2 kcal kcal mol– 1, an improvement over the results of the nonpolarizable force field, but still signficantly different than the experimental value of –30.1 kcal mol–1. The combined QM/MM approach performs much better, predicting a ΔΔGhydr of –34.8 kcal mol–1 in excellent agreement with experiment. These calculations support the experimental observation that Zn2+ has more favourable solvation free energy than Mg2+ despite having a very similar solvation structure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".