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Record W4403445782 · doi:10.1107/s2052520624007492

The seventh blind test of crystal structure prediction: structure generation methods

2024· article· en· W4403445782 on OpenAlexafffund
Lily M. Hunnisett, Jonas Nyman, Nicholas Francia, Nathan S. Abraham, Claire S. Adjiman, Srinivasulu Aitipamula, Tamador Alkhidir, Mubarak Almehairbi, Andrea Anelli, Dylan M. Anstine, John E. Anthony, Joseph E. Arnold, Faezeh Bahrami, Michael A. Bellucci, Rajni M. Bhardwaj, Imanuel Bier, J.A. Bis, A. Daniel Boese, David Bowskill, James Bramley, Jan Gerit Brandenburg, Doris E. Braun, Patrick W. V. Butler, Joseph Cadden, Stephen A. R. Carino, Eric J. Chan, Chao Chang, Bingqing Cheng, Sarah Melanie Clarke, Simon J. Coles, Richard I. Cooper, Ricky Wayne Couch, Raúl Cuadrado‐Matías, Tom Darden, Graeme M. Day, H. Dietrich, Yiming Ding, Antonio G. DiPasquale, Bhausaheb Dhokale, Bouke P. van Eijck, M.R.J. Elsegood, Dzmitry S. Firaha, Wenbo Fu, Kaori Fukuzawa, Joseph Glover, Midori Goto, Chandler Greenwell, J. A. Harter, Julian Helfferich, Detlef W. M. Hofmann, Johannes Hoja, John Hone, Richard S. Hong, Geoffrey Hutchison, Yasuhiro Ikabata, Olexandr Isayev, Ommair Ishaque, Yingdi Jin, Aling Jing, Erin R. Johnson, Ian M. Jones, K. V. Jovan Jose, Elena A. Kabova, Adam C. Keates, Paul F. Kelly, Dmitry V. Khakimov, Stefanos Konstantinopoulos, L. N. Kuleshova, He Li, Xiaolu Lin, Alexander List, Congcong Liu, Yifei Michelle Liu, Zenghui Liu, Zhi‐Pan Liu, Joseph W. Lubach, Noa Marom, Alexander A. Maryewski, Hiroyuki Matsui, Alessandra Mattei, R. Alex Mayo, John W. Melkumov, Sharmarke Mohamed, Zahrasadat Momenzadeh Abardeh, Hari S. Muddana, Naofumi Nakayama, Kamal Singh Nayal, Marcus A. Neumann, Rahul Nikhar, Shigeaki Obata, Dana O’Connor, Artem R. Oganov, Koji Okuwaki, Alberto Otero‐de‐la‐Roza, Constantinos C. Pantelides, Chris J. Pickard, Luca Pilia, T. S. Pivina, Rafał Podeszwa, Alastair J. A. Price, Louise S. Price, Sarah L. Price, Michael R. Probert, Angeles Pulido, Gunjan Rajendra Ramteke, Attiq-Ur Rehman, Susan M. Reutzel‐Edens, Jutta Rogal, Marta J. Ross, Adrian F. Rumson, G. Sadiq, Zeinab M. Saeed, Alireza Salimi, Matteo Salvalaglio, LETICIA SANDERS DE ALMADA, Kiran Sasikumar, Sivakumar Sekharan, Cheng Shang, Kenneth Shankland, Kotaro Shinohara, Baimei Shi, Xuekun Shi, A. Geoffrey Skillman, Hongxing Song, Nina Strasser, Jacco van de Streek, Isaac J. Sugden, Guangxu Sun, Krzysztof Szalewicz, Benjamin I. Tan, Lu Tan, Frank Tarczynski, Christopher R. Taylor, Alexandre Tkatchenko, Rithwik Tom, Mark E. Tuckerman, Yohei Utsumi, Leslie Vogt-Maranto, Jake Weatherston, Lawrence H. Wilkinson, Robert D. Willacy, Łukasz Wojtas, Grahame R. Woollam, Zhuocen Yang, Etsuo Yonemochi, Xin Yue, Qun Zeng, Y. Zhang, Tian Zhou, Yunfei Zhou, R.I. Zubatyuk, Jason C. Cole

Bibliographic record

VenueActa Crystallographica Section B Structural Science Crystal Engineering and Materials · 2024
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsDalhousie University
FundersArmy Research LaboratoryArmy Research OfficeDivision of ChemistryDivision of Materials ResearchAgencia Estatal de InvestigaciónEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaScience and Technology Facilities CouncilHorizon 2020 Framework ProgrammeRussian Science FoundationEuropean CommissionMinisterio de Ciencia e InnovaciónGenentechFundación para el Fomento en Asturias de la Investigación Científica Aplicada y la TecnologíaNational Natural Science Foundation of ChinaUniversity of SouthamptonUniversity College LondonUK Research and InnovationNational Science FoundationImperial College LondonSyngenta InternationalXtalPiUniversität InnsbruckKhalifa University of Science, Technology and ResearchDell EMCKarl-Franzens-Universität GrazDeutsche ForschungsgemeinschaftEli Lilly and CompanyAlliance de recherche numérique du CanadaAir Force Office of Scientific ResearchNational Key Research and Development Program of ChinaUniversity of Reading
KeywordsCrystal structure predictionCocrystalCrystal structureCrystallographyPowder diffractionMoleculeCrystal (programming language)Materials scienceRietveld refinementChemistryComputer scienceHydrogen bondOrganic chemistry

Abstract

fetched live from OpenAlex

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

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)0.083
metaresearch head score (Gemma)0.244
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 score0.083
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.244
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0030.005
Scholarly communication0.0050.006
Open science0.0040.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.004

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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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

Citations79
Published2024
Admission routes2
Has abstractyes

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