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Record W4320728701 · doi:10.26434/chemrxiv-2023-b7f0j

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

2023· preprint· en· W4320728701 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
FundersOak Ridge National LaboratoryBasic Energy SciencesNational Supercomputer Centre, Linköpings UniversitetUppsala Multidisciplinary Center for Advanced Computational ScienceEuropean Regional Development FundOffice of ScienceEuropean CommissionMinisterio de Ciencia e InnovaciónRoyal SocietyAlliance de recherche numérique du CanadaMinistero dell’Istruzione, dell’Università e della RicercaUppsala UniversitetCentre National de la Recherche ScientifiqueU.S. Department of EnergyVetenskapsrådetLinköpings UniversitetLunds UniversitetPartnership for Advanced Computing in Europe AISBLDeutsche ForschungsgemeinschaftGrand Équipement National De Calcul IntensifNational Science CouncilCarl Tryggers Stiftelse för Vetenskaplig ForskningUniversity of ChicagoEuropean Cooperation in Science and TechnologyUniversity of ManchesterFundación Bancaria Caixa d'Estalvis i Pensions de BarcelonaUniversity of MinnesotaNational Science FoundationAgencia Estatal de InvestigaciónSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsComputer scienceFocus (optics)Electronic structureSoftwareComputational scienceChemistryNanotechnologyData scienceComputational chemistryPhysicsMaterials scienceProgramming language

Abstract

fetched live from OpenAlex

In this article the recent developments of the open-source OpenMolcas chemistry software environment, since spring 2020, are described, with the main focus on novel functionalities that are accessible in the stable branch of the package and/or via interfaces with other packages. These community developments span a wide range of topics in computational chemistry, and are presented in thematic sections associated with electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report represents a useful summary of these developments, and it offers a solid overview of the chemical phenomena and processes that OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0040.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.039
GPT teacher head0.307
Teacher spread0.268 · 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