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Record W2007237400 · doi:10.1139/v09-071

A test of various computational solvation models on a set of “difficult” organic compounds

2009· article· en· W2007237400 on OpenAlexaffvenue
J. Peter Guthrie, Igor Povar

Bibliographic record

VenueCanadian Journal of Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistrySolvationHalogenComputational chemistryImplicit solvationBasis setMoleculeGaussianSolventPhysical chemistryOrganic chemistryAlkyl

Abstract

fetched live from OpenAlex

Various dielectric continuum models in Gaussian 03, based on the SCRF approach, PCM, CPCM, DPCM, IEFPCM, IPCM, and SCIPCM, have been tested on a set of 54 highly polar, generally polyfunctional compounds for which experimental solvation energies are available. These compounds span a range of 13 kcal/mol in ΔG t . The root-mean-square (RMS) errors for the full set of compounds range from 2.48 for DPCM to 1.77 for IPCM. For each method, classes of compounds which were not handled well could be identified. If these classes of compounds were omitted, the performance improved, and ranged from 1.58 (PCM, 39 compounds) to 1.02 (IPCM, 42 compounds). Models in the PCM family (PCM, CPCM, DPCM, and IEFPCM) with the recommended UAHF or UAKS sets of radii rely on a highly parameterized definition of the solvent cavity. Where this parameterization was inadequate, the calculated solvation energies were less reliable. This has been demonstrated by devising a new parameterization for PCM and halogen compounds, which markedly improves performance for polyhalogen compounds. The effective radius for the portion of the cavity centered on a halogen atom was assumed to be linear in the electron-withdrawing or -donating properties of the rest of the molecule as measured by Hammett σ (for halogens on aromatic rings) or Taft σ* (for halogens on aliphatic carbons). This new parameterization for PCM was tested on a set of 45 aliphatic and 22 aromatic polyhalogen compounds and shown to do well. IPCM, which was already the best of the methods in Gaussian, can be considerably improved by a parameterization to allow for cavitation, dispersion, and hydrogen bonding. A large set of compounds was used for the parameterization to have multiple examples for each parameter and as far as possible to have molecules with multiple instances of each structural feature. In the end, 15 parameters were found to be defined by the data for 241 compounds. With this parameter set, the RMS error for the set used for fitting was 0.81 kcal/mol, and the RMS error for the original set of 54 compounds was 0.85. With this new parameterization, IPCM is clearly the best of the methods available in Gaussian 03.

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 score0.013
Threshold uncertainty score0.489

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations23
Published2009
Admission routes2
Has abstractyes

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