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Record W2237719956 · doi:10.5488/cmp.18.32601

Molecular theory of solvation: Methodology summary and illustrations

2015· article· en· W2237719956 on OpenAlexfundno aff

Bibliographic record

VenueCondensed Matter Physics · 2015
Typearticle
Languageen
FieldChemistry
TopicAdvanced Physical and Chemical Molecular Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDivision of Graduate EducationAlberta Innovates Bio SolutionsArboraNanoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAlberta InnovatesFundação de Amparo à Pesquisa do Estado de São PauloUniversity of AlbertaWestern Canada Research GridCompute Canada
KeywordsSolvationIntegral equationDiagrammatic reasoningClosure (psychology)Thermodynamic integrationStatistical mechanicsMolecular dynamicsDensity functional theoryField (mathematics)

Abstract

fetched live from OpenAlex

Integral equation theory of molecular liquids based on statistical mechanics is quite promising as an essential part of multiscale methodology for chemical and biomolecular nanosystems in solution.Beginning with a molecular interaction potential force field, it uses diagrammatic analysis of the solvation free energy to derive integral equations for correlation functions between molecules in solution in the statistical-mechanical ensemble.The infinite chain of coupled integral equations for many-body correlation functions is reduced to a tractable form for 2-or 3-body correlations by applying the so-called closure relations.Solving these equations produces the solvation structure with accuracy comparable to molecular simulations that have converged but has a critical advantage of readily treating the effects and processes spanning over a large space and slow time scales, by far not feasible for explicit solvent molecular simulations.One of the versions of this formalism, the threedimensional reference interaction site model (3D-RISM) integral equation complemented with the Kovalenko-Hirata (KH) closure approximation, yields the solvation structure in terms of 3D maps of correlation functions, including density distributions, of solvent interaction sites around a solute (supra)molecule with full consistent account for the effects of chemical functionalities of all species in the solution.The solvation free energy and the subsequent thermodynamics are then obtained at once as a simple integral of the 3D correlation functions by performing thermodynamic integration analytically.Analytical form of the free energy functional permits the self-consistent field coupling of 3D-RISM-KH with quantum chemistry methods in multiscale description of electronic structure in solution, the use of 3D maps of potentials of mean force as scoring functions for molecular recognition and protein-ligand binding in docking protocols for fragment based drug design, and the hybrid MD simulation running quasidynamics of biomolecules steered with 3D-RISM-KH mean solvation forces.The 3D-RISM-KH theory has been validated on both simple and complex associating liquids with different chemical functionalities in a wide range of thermodynamic conditions, at different solid-liquid interfaces, in soft matter, and various environments and confinements.The 3D-RISM-KH theory offers a "mental microscope" capable of providing an insight into structure and molecular mechanisms of formation and functioning of various chemical and biomolecular systems and nanomaterials.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0200.016

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 head0.063
GPT teacher head0.301
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations37
Published2015
Admission routes1
Has abstractyes

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