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Record W4412440580 · doi:10.1021/acs.jpca.5c02171

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

2025· review· en· W4412440580 on OpenAlexaff
Léon L. E. Cigrang, Basile F. E. Curchod, Rebecca A. Ingle, Aaron Kelly, Jonathan R. Mannouch, Davide Accomasso, Alexander Alijah, Mario Barbatti, Wiem Chebbi, Nađa Došlić, Elliot C. Eklund, Sebastian Fernández-Alberti, Antonia Freibert, Leticia González, Giovanni Granucci, Federico J. Hernández, Javier Hernández-Rodríguez, Amber Jain, Jiří Janoš, Ivan Kassal, Adam Kirrander, Zhenggang Lan, Henrik R. Larsson, David Lauvergnat, Brieuc Le Dé, Neepa T. Maitra, Seung Kyu Min, Daniel Peláez, David Picconi, Umberto Raucci, Patrick A. Robertson, Eduarda Sangiogo Gil, Marin Sapunar, Peter Schürger, Patrick Sinnott, Sergei Tretiak, Arkin Tikku, Patricia Vindel-Zandbergen, Graham A. Worth, Federica Agostini, Sandra Gómez, Lea M. Ibele, Antonio Prlj

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2025
Typereview
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsCanadian Nautical Research Society
FundersH2020 Marie Skłodowska-Curie ActionsLos Alamos National LaboratoryH2020 European Research CouncilBasic Energy SciencesOffice of Naval ResearchOffice of Naval Research GlobalCenter for Integrated NanotechnologiesAustralian Research CouncilOffice of ScienceLaboratoire d’excellence Physique Atomes Lumière MatièreHrvatska Zaklada za ZnanostNarodowe Centrum NaukiAgence Nationale de la RechercheDivision of ChemistryUniversity of NottinghamU.S. Department of EnergyEuropean CommissionGrantová Agentura České RepublikyAmerican Chemical SocietyLeverhulme TrustDeutsche ForschungsgemeinschaftEngineering and Physical Sciences Research CouncilNational Science FoundationUK Research and InnovationAmerican Chemical Society Petroleum Research FundDepartment of Education and TrainingAix-Marseille UniversitéSchool of Chemistry, University of NottinghamAlexander von Humboldt-Stiftung
KeywordsMolecular dynamicsBenchmark (surveying)Computer scienceField (mathematics)ObstaclePerspective (graphical)Task (project management)Set (abstract data type)Work (physics)Systems engineeringChemistryPhysicsArtificial intelligenceComputational chemistryEngineeringQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

, was held in May 2024 to address this issue. This Perspective highlights the key challenges identified during the workshop in defining molecular benchmarks for nonadiabatic dynamics. Specifically, this work outlines some preliminary observations on essential components needed for simulations and proposes a roadmap aiming to establish, as an ultimate goal, a community-driven, standardized molecular benchmark set.

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)0.015
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0030.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0100.004

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 head0.009
GPT teacher head0.317
Teacher spread0.308 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations26
Published2025
Admission routes1
Has abstractyes

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