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Record W4220943645 · doi:10.1038/s41467-022-29054-5

Hypervalency in amorphous chalcogenides

2022· article· en· W4220943645 on OpenAlexaff
T. H. Lee, Stephen R. Elliott

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsTrinity College
FundersEngineering and Physical Sciences Research CouncilResearch Councils UK
KeywordsHypervalent moleculeChalcogenideAmorphous solidChemical physicsLone pairAb initioMoleculeMaterials scienceNanotechnologyComputational chemistryCrystallographyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The concept of hypervalency emerged as a notion for chemical bonding in molecules to explain the atomic coordination in hypervalent molecules that violates the electron-octet rule. Despite its significance, however, hypervalency in condensed phases, such as amorphous solids, remains largely unexplored. Using ab initio molecular-dynamics simulations, we report here the underlying principles of hypervalency in amorphous chalcogenide materials, in terms of the behaviour of hypervalent structural units, and its implicit relationship with material properties. The origin of a material-dependent tendency towards hypervalency is made evident with the multi-centre hyperbonding model, from which its relationship to abnormally large Born effective charges is also unambiguously revealed. The hyperbonding model is here extended to include interactions with cation s2 lone pairs (LPs); such deep-lying LPs can also play a significant role in determining the properties of these chalcogenide materials. The role of hypervalency constitutes an indispensable and important part of chemical interactions in amorphous and crystalline chalcogenide solids.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.301
Teacher spread0.262 · 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 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

Citations47
Published2022
Admission routes1
Has abstractyes

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