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Record W4319925007 · doi:10.1002/adma.202370042

A Materials Acceleration Platform for Organic Laser Discovery (Adv. Mater. 6/2023)

2023· article· en· W4319925007 on OpenAlexaff
Tony Wu, Andrés Aguilar‐Granda, Kazuhiro Hotta, Sahar Alasvand Yazdani, Robert Pollice, Jenya Vestfrid, Han Hao, Cyrille Lavigne, Martin Seifrid, Nicholas H. Angello, Fatima Bencheikh, Jason E. Hein, Martin D. Burke, Chihaya Adachi, Alán Aspuru‐Guzik

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceAccelerationLaserNanotechnologyProperty (philosophy)Organic moleculesMoleculeOpticsOrganic chemistryPhysicsChemistry

Abstract

fetched live from OpenAlex

Accelerated Materials Discovery In article number 2207070, Tony C Wu, Alán Aspuru-Guzik, and co-workers report a materials acceleration platform to automatically search for high-performing organic laser molecules, which includes synthesis, product identification, and property measurements. This platform has discovered two state-of-the-art organic lasers from searching through 40 potential candidates. The results show the potential of automated synthesis and accelerated discovery of materials.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.008

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 head0.031
GPT teacher head0.305
Teacher spread0.275 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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