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Record W4400115450 · doi:10.1063/1674-0068/cjcp2404052

Electronic circular dichroism spectra calculation based on generalized energy-based fragmentation approach

2024· article· en· W4400115450 on OpenAlexaff

Bibliographic record

VenueChinese Journal of Chemical Physics · 2024
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsFragmentation (computing)Circular dichroismSpectral lineVibrational circular dichroismComputational physicsPhysicsAtomic physicsMolecular physicsMaterials scienceChemistryComputer scienceCrystallographyQuantum mechanics

Abstract

fetched live from OpenAlex

Electronic circular dichroism (ECD) spectrum is an important tool for assessing molecular chirality. Traditional methods, like linear response time-dependent density functional theory (LR-TDDFT), predict ECD spectra well for small or medium-sized molecules, but struggle with large systems due to high computational costs, making it a significant challenge to accurately and efficiently predict the ECD properties of complex systems. Within the framework of the generalized energy-based fragmentation (GEBF) method for localized excited states (ESs) calculation, we propose a combination algorithm for calculating rotatory strengths of ESs in condensed phase systems. This algorithm estimates the rotatory strength of the total system by calculating and combining the transition electric and magnetic dipole moments of subsystems. We have used the GEBF method to calculate the ECD properties of chiral drug molecule derivatives, green fluorescent protein, and cyclodextrin derivatives, and compared their results with traditional methods or experimental data. The results show that this method can efficiently and accurately predict the ECD spectra of these systems. Thus, the GEBF method for ECD spectra demonstrates great potential in the chiral analysis of complex systems and chiral material design, promising to become a powerful theoretical tool in chiral chemistry.

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 score0.243
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.255
Teacher spread0.248 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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