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The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry
Giovanni Li Manni, Ignacio Fdez. Galván, Ali Alavi, Flavia Aleotti, Francesco Aquilante, Jochen Autschbach, Davide Avagliano, Alberto Baiardi, Jie Bao, Stefano Battaglia, Letitia Birnoschi, Alejandro Blanco-González, Sergey I. Bokarev, Ria Broer, Roberto Cacciari, Paul B. Calio, Rebecca K. Carlson, Rafael C. Couto, Luis Cerdán, Liviu F. Chibotaru, Nicolas F. Chilton, Jonathan R. Church, Irene Conti, Sonia Coriani, Juliana Cuéllar-Zuquin, Razan E. Daoud, Nike Dattani, Piero Decleva, Coen de Graaf, Mickaël G. Delcey, Luca De Vico, Werner Dobrautz, Sijia S. Dong, Rulin Feng, Nicolas Ferré, Michael Filatov, Laura Gagliardi, Marco Garavelli, Leticia González, Yafu Guan, Meiyuan Guo, Matthew R. Hennefarth, Matthew R. Hermes, Chad E. Hoyer, Miquel Huix‐Rotllant, Vishal Kumar Jaiswal, Andy Kaiser, Danil Kaliakin, Marjan Khamesian, Daniel S. King, Vladislav Kochetov, Marek Krośnicki, Arpit Arun Kumaar, Ernst D. Larsson, Susi Lehtola, Marie-Bernadette Lepetit, Hans Lischka, Pablo López Ríos, Marcus Lundberg, Dongxia Ma, Sebastian Mai, Philipp Marquetand, Isabella C. D. Merritt, Francesco Montorsi, Maximilian Mörchen, Artur Nenov, Yoshio Nishimoto, Meagan S. Oakley, Massimo Olivucci, Markus Oppel, D. D. La Padula, Riddhish Pandharkar, Quan Manh Phung, Felix Plasser, Gerardo Raggi, Elisa Rebolini, Markus Reiher, Ivan Rivalta, Daniel Roca‐Sanjuán, Thies Romig, Arta Anushirwan Safari, Aitor Sánchez-Mansilla, Andrew M. Sand, Igor Schapiro, Thais R. Scott, Javier Segarra‐Martí, Francesco Segatta, Dumitru‐Claudiu Sergentu, Prachi Sharma, Ron Shepard, Yinan Shu, Jakob K. Staab, Tjerk P. Straatsma, Lasse Kragh Sørensen, Bruno Nunes Cabral Tenorio, Donald G. Truhlar, Liviu Ungur, Morgane Vacher, Valera Veryazov, Torben Arne Voß, Oskar Weser, Dihua Wu, Xuchun Yang, David R. Yarkony, Chen Zhou, J. Patrick Zobel, Roland Lindh
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
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.989
Threshold uncertainty score0.151
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.045 | 0.026 |
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 status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
Study designNot applicable
Domainnot available
GenreMethods
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
Citations15
Published2023
Admission routes1
Has abstractyes