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Record W4391733802 · doi:10.1063/5.0189211

The structural and electronic split: Boron vs aluminum hydrides

2024· article· en· W4391733802 on OpenAlexfundno aff
Anton S. Pozdeev, Ivan A. Popov

Bibliographic record

VenueChemical Physics Reviews · 2024
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsnot available
FundersCanadian Centre for Applied Research in Cancer ControlUniversity of Akron
KeywordsBoronAluminiumMaterials scienceCrystallographyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

We systematically investigated the structural evolution of boron (B) and aluminum (Al) hydrides using various DFT and ab initio methods, aiming to reveal the similarities and differences in their geometric and electronic structures. While B hydrides have been extensively studied both experimentally and theoretically, less is known about its group 13 heavier congener, Al. Extensive global minimum searches of the B2Hx (Al2Hx) and B3Hy (Al3Hy) hydrides (x = [0–6], y = [0–9]) were performed to identify the most stable geometric structures for each stoichiometry. In most of the series, B and Al hydrides exhibit qualitatively different structures, except for the most saturated X2H5 and X2H6 stoichiometries. Chemical bonding analyses employing adaptive natural density partitioning and electron localization function methods identified notable differences between B and Al hydrides in most of the compositions. B hydrides predominantly possess two-center (2c) and three-center (3c) bonding elements, suggesting a relatively balanced electron distribution. On the contrary, Al hydrides tend to retain unpaired electrons or lone pairs on Al atoms, forming a large number of closely lying isomers with various combinations of 1c, 2c, 3c, and 4c bonding elements. Thermodynamic stability analyses revealed that all studied clusters demonstrated stability toward various H/H2 dissociation pathways, with Al hydrides being less stable than B counterparts.

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)0.001
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 score0.215
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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