An efficient and accurate model for water with an improved non-bonded potential
Bibliographic record
Abstract
A molecular mechanical model for liquid water is developed that uses a physically motivated potential to represent Pauli repulsion and dispersion instead of the standard Lennard-Jones potential. The model has three atomic sites and a virtual site located on the ∠HOH bisector (i.e., a TIP4P-type model). Pauli-repulsive interactions are represented using a Buckingham-type exponential decay potential. Dispersion interactions are represented by both C6/r6 and C8/r8 terms. This higher order C8 dispersion term has been neglected by most force fields. The ForceBalance code was used to define parameters that optimally reproduce the experimental physical properties of liquid water. The resulting model is in good agreement with the experimental density, dielectric constant, enthalpy of vaporization, isothermal compressibility, thermal expansion coefficient, diffusion coefficient, and radial distribution function. A graphical processing unit-accelerated implementation of this improved non-bonded potential can be employed in OpenMM without modification by using the CustomNonBondedForce feature. The efficient and automated parameterization of these non-bonded potentials provides a rational strategy to define a new molecular mechanical force field that treats repulsion and dispersion interactions more rigorously without major modifications to the existing simulation codes or a substantially larger computational cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".