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Record W2754217508 · doi:10.1039/c7cp05585d

Multiscale methods framework: self-consistent coupling of molecular theory of solvation with quantum chemistry, molecular simulations, and dissipative particle dynamics

2017· article· en· W2754217508 on OpenAlexaff
Andriy Kovalenko, Sergey Gusarov

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsSolvationDissipative particle dynamicsCoupling (piping)GranularityStatistical physicsField (mathematics)Molecular dynamicsDissipative systemImplicit solvationRange (aeronautics)QuantumWork (physics)ChemistryComputer scienceNanotechnologyPhysicsComputational chemistryMaterials scienceMoleculeThermodynamicsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

In this work, we will address different aspects of self-consistent field coupling of computational chemistry methods at different time and length scales in modern materials and biomolecular science. Multiscale methods framework yields dramatically improved accuracy, efficiency, and applicability by coupling models and methods on different scales. This field benefits many areas of research and applications by providing fundamental understanding and predictions. It could also play a particular role in commercialization by guiding new developments and by allowing quick evaluation of prospective research projects. We employ molecular theory of solvation which allows us to accurately introduce the effect of the environment on complex nano-, macro-, and biomolecular systems. The uniqueness of this method is that it can be naturally coupled with the whole range of computational chemistry approaches, including QM, MM, and coarse graining.

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 categoriesMeta-epidemiology (narrow)
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 score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.315
Teacher spread0.302 · 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.

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

Citations31
Published2017
Admission routes1
Has abstractyes

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