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Record W4385550399 · doi:10.26434/chemrxiv-2023-w65hb

Rational Design of Organic Molecules with Inverted Gaps between First Excited Singlet and Triplet

2023· preprint· en· W4385550399 on OpenAlexafffund
Robert Pollice, Benjamin Ding, Alán Aspuru‐Guzik

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsVector InstituteUniversity of Toronto
FundersNatural Resources CanadaUniversity of TorontoGovernment of OntarioCanada First Research Excellence FundSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsExcited stateSinglet fissionSinglet stateHOMO/LUMOMoleculeChemical physicsMolecular orbitalChemistryPopulationExcitonMolecular physicsAtomic physicsPhysicsCondensed matter physics

Abstract

fetched live from OpenAlex

One implication of Hund’s first rule for molecules is that the first excited triplet is for the large majority of molecules more stable than the first excited singlet. Only a handful of structural motifs are known to violate it. Recently, the potential of such molecules for emissive materials has been appreciated as their inverted singlet-triplet gaps enable favorable triplet exciton harvesting and low triplet exciton population to be utilized in both efficient and long-lived organic light-emitting diodes. Herein, we report bottom-up design of small organic molecules with inverted singlet-triplet gaps, i.e., INVEST molecules, via the Ring-Bonds-Substitutions (RiBS) ruleset which we introduce in this work. It relies on a few simple rules based on orbital interactions in alternant hydrocarbons to both minimize the exchange integral between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and maximize dynamic spin polarization. Using RiBS, we find many new molecules violating Hund’s first rule in their first excited states thereby greatly expanding the chemical space of potential inverted singlet-triplet gap (INVEST) emitters.

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 score0.114
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
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.242
Teacher spread0.198 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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