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Record W2062888085 · doi:10.1063/1.4801322

Molecular force fields for aqueous electrolytes: SPC/E-compatible charged LJ sphere models and their limitations

2013· article· en· W2062888085 on OpenAlexaff
Filip Moučka, Ivó Nezbeda, William R. Smith

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAqueous solutionElectrolyteMaterials scienceStatistical physicsChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Thirteen of the most common aqueous NaCl solution force fields based on the SPC/E water solvent are examined with respect to their prediction at ambient conditions of the concentration dependence of the total electrolyte chemical potential and the solution density. We also calculate the salt solubility and the chemical potential and density of the NaCl crystalline solid. We obtain the solution chemical potential in a computationally efficient manner using our recently developed Osmotic Ensemble Monte Carlo method [F. Moučka, M. Lísal, and W. R. Smith, J. Phys. Chem. B 116, 5468 (2012)]. We find that the results of the force fields considered are scattered over a wide range of values, and none is capable of producing quantitatively accurate results over the entire concentration range, with only two of them deemed to be acceptable. Our results indicate that several force fields exhibit precipitation at concentrations below the experimental solubility limit, thus limiting their usefulness. This has important implications, both in general and for their use in biomolecular simulations carried out in the presence of counter-ions. We conclude that either different parameter fitting techniques taking high-concentration properties into account must be used when determining force field model parameters, or that the class of models considered here is intrinsically incapable of the task and more sophisticated mathematical forms must be used.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.241
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations95
Published2013
Admission routes1
Has abstractyes

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