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

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Author(s)Hunnisett, Lily M.
Author(s)Nyman, Jonas
Author(s)Francia, Nicholas
Author(s)Abraham, Nathan S.
Author(s)et al.
Date Accessioned2024-10-18T19:53:45Z
Date Available2024-10-18T19:53:45Z
Publication Date2024-12-01
DescriptionPlease see publication for complete list of co-authors. This article was originally published in Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials. The version of record is available at: https://doi.org/10.1107/S2052520624007492. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
AbstractA 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.
SponsorThe CCDC Blind Test Team. The CCDC organizers (L. M. Hunnisett, J. Nyman, N. Francia, I. Sugden, G. Sadiq, and J. C. Cole) gratefully acknowledge numerous CCDC colleagues for their helpful feedback and suggestions on the manuscript (P. McCabe, E. Pidcock, P. Martinez-Bulit, C. Kingsbury), providing useful python knowledge (A. Moldovan), providing and maintaining internal compute resources (K. Taylor, M. Burling, J. Swift, L. Wallis), monitoring and depositing structures in the CSD (S. Ward, K. Orzechowska, V. Menon), support in organization of the blind test meeting (E. Clarke), and improvements to the Crystal Packing Similarity tool (M. Read). Data analysis was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). N. Francia thanks M. Salvalaglio for advice on the metadynamics simulations and the University College London for providing access to the Kathleen High Performance Computing Facility (Kathleen@UCL) on which simulations were performed. N. Francia also thanks V. Kurlin and D. E. Widdowson for counselling on crystal structure similarity. I. Sugden and N. Francia participated in the blind test as members of Groups 1 and 24, respectively. They were involved in the analysis of the results and in writing this paper only after all results were made available to participants. Group 1. Funding for this research was provided by: Engineering and Physical Sciences Research Council (grant Nos. EP/J014958/1, EP/J003840/1, EP/P022561/1, EP/P020194, and EP/T51780X/1), Eli Lilly and Company and Syngenta. We would like to acknowledge the Imperial College Research Computing Service, DOI: 10.14469/hpc/2232, the Cirrus UK National Tier-2 HPC Service at EPCC (https://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1), and the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1). Group 3. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC) as well as the HPC facilities at the University of Graz and the University of Innsbruck. Group 5. We thank the University of Southampton for a University of Southampton Presidential Scholarship (Patrick W. V. Butler), Johnson Matthey for funding (James Bramley), the Air Force Office of Scientific Research for funding under award No. FA8655-20-1-7000 (Joseph E. Arnold) and the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No. 856405) (Christopher Taylor, Ramon Cuadrado, Joseph Glover, Graeme M. Day). We acknowledge the use of the IRIDIS High-Performance Computing Facility and associated support services at the University of Southampton. Via our membership of the UK's HEC Materials Chemistry Consortium, which is funded by the EPSRC (EP/R029431), this work used the UK Materials and Molecular Modelling Hub for computational resources, the MMM Hub, which is partially funded by the EPSRC (EP/P020194/1 and EP/T022213/1). Group 6. Toine Schreurs and Martin Lutz provided computer facilities and assistance. Group 8. We would like to thank the CCDC for their support. Group 10. Competing interests: Many authors work at XtalPi Inc., a company that provide crystal structure prediction services. We would also like to thank other platform builders in our group. Although they did not directly participate in this blind test, some of them contributed to the construction of our early platform, and some of them contributed to the stable operation of our computing system. They are: Peiyu Zhang, Minjun Yang, Yang Liu, Dong Fang, Bochen Li, Jiuchuang. Yuan, Ziqi Jiang, Xiaoqi Kang, Fei Li, Yanpeng Ma, Wenpeng Mei, Liang Tan, Huobin Wang, Hesheng Zhu. Group 11. ERJ thanks the Natural Sciences and Engineering Council (NSERC) of Canada for funding. RAM thanks the Walter C. Sumner Foundation for financial support. AOR thanks: the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación, project PGC2021-125518NB-I00 co-financed by EU FEDER funds; the Principality of Asturias (FICYT), project AYUD/2021/51036 cofinanced by EU FEDER; and the Spanish MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR for grant TED2021-129457B-I00. ERJ, AOR, RAM, SMC, AFR, and AJAP are grateful to the Digital Research Alliance of Canada (DRAC) and, particularly, to ACENET for providing computational resources. Group 13. The authors are deeply grateful to Dr Alexandr V. Dzyabchenko for the provided programs for crystal structures simulation. The supercomputer resources were provided by the HPC centers of N. D. Zelinsky IOC RAS and `MVS100K' of the Russian Academy of Science. Group 16. The Isayev group acknowledges support from NSF CHE-1802789 and CHE-2041108. We also acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) award CHE200122, which is supported by NSF grant number ACI-1053575. This research is part of the Frontera computing project at the Texas Advanced Computing Center. Frontera is made possible by the National Science Foundation award OAC-1818253. This research in part was done using resources provided by the Open Science Grid, which is supported by the award 1148698, and the US DOE Office of Science. The Marom group acknowledges support from National Science Foundation (NSF) through grant DMR-2131944. This research used resources of Argonne Leadership Computing Facility (ALCF), which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We also acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) award MAT210006, which supported 3 million central processing unit (CPU) core hours. Group 17. This work was partly supported by JST CREST (Grant Number: JPMJCR18J2). Group 18. Financial support for this work was made possible by Khalifa University (KU) under the Research and Innovation Grant (Award No. RIG-2023-054). This work was performed with the support of the Center for Catalysis and Separations (RC2-2018-024). All the computational calculations were performed using the High-Performance Computing (HPC) clusters of KU and the authors acknowledge the support of the Research Computing Department. Finally, we thank Professor Costas Pantelides and Professor Claire S. Adjiman for providing access to the CrystalPredictor code. SM would also like to express sincere gratitude to Dr Isaac J. Sugden for providing technical support on the use of CrystalPredictor. Group 19. OpenEye thanks Amazon Web Services for providing computational resources. As a CSP solution provider to the pharmaceutical industry, OpenEye declares a conflict of interests. Group 20. Competing interests: MAN is the founder, owner, and director, and DF, YML, JvdS, KS, and HD are employees of Avant-garde Materials Simulation Deutschland GmbH (AMS), a software company specializing in organic crystal structure prediction, and have no additional conflict of interest to disclose. Group 21. SO and HG thank Professor Dr S. L. Price for her valuable advice on our prediction of XXX. In this work, we used the computer resources by Research Institute for Information Technology, Kyushu University, ACCMS, Kyoto University, and Information and Media Center, Toyohashi University of Technology. Part of this work used computational resources of Fugaku supercomputer through the HPCI System Research Project (Project ID: hp220143). The FMO calculations were performed in the activities of the FMO drug design consortium (FMODD). This work was supported by JSPS KAKENHI Grant Nos. 17 H06373 (HG), 21 K05002 (YI), and 21 K05105 (NN). HG is in a conflict of interest because he is the first developer of the software (CONFLEX) used in this paper and is a board member of the company that developed and distributes it. Group 22. Group 22 acknowledges support from the Russian Science Foundation (grant 19-72-30043). Competing interests: the USPEX code is free for academic researchers, but is distributed at a fee to companies. Group 24. Funding from the European Union's Horizon 2020 Research and Innovation program under Grant Agreement Number 736899 (MagnaPharm), Eli Lilly Digital Design, the EPSRC via the UKRI Frontier Research Guarantee Grant number EP/X033139/1 (ht-MATTER: high-throughput Modelling of Molecular Crystals Out of Equilibrium). Group 25. This work received financial support from the National Science Foundation of China (21603035) and the National Key Research and Development Program of China (2018YFA0208600). Groups 26 and 27. The work at the University of Delaware was supported by the US Army Research Laboratory and Army Research Office under grant W911NF-19-0117 and National Science Foundation under grants CHE-1900551, CHE-2154908, and CHE-2313826. JR acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG) through the Heisenberg Programme project 428315600. JR and MET acknowledge funding from the National Science Foundation grant DMR-2118890. MET acknowledges support from the National Science Foundation, grant No. CHE-1955381. Reading group. We thank the University of Reading's Chemical Analysis Facility for the instrumentation used in the collection of diffraction data from crystals of structure XXIX, and the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/T022213/1, EP/W032260/1 and EP/P020194/1), for computational resources. Joanna Bis. Acknowledges Gnel Mkrtchyan and Joshua Hoerner from Purisys (formerly Noramco) for providing the study materials and encouragement for the publications. John Anthony, Sean Parkin. Material synthesis and structure characterization were supported by the National Science Foundation, under grants DMR-1627428 and CHE-1625732. Genentech group. Conflict of interest: While some of the test molecules may have originated with customers or potential customers of some of the commercial code providers, no ex parte communication on the molecules, structures, or forms occurred to ensure a level playing field for all participants.
CitationHunnisett, Lily M., Jonas Nyman, Nicholas Francia, Nathan S. Abraham, Claire S. Adjiman, Srinivasulu Aitipamula, Tamador Alkhidir, et al. “The Seventh Blind Test of Crystal Structure Prediction: Structure Generation Methods.” Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 80, no. 6 (December 1, 2024). https://doi.org/10.1107/S2052520624007492.
ISSN2052-5206
URLhttps://udspace.udel.edu/handle/19716/35275
Languageen_US
PublisherActa Crystallographica Section B: Structural Science, Crystal Engineering and Materials
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
Keywordscrystal structure prediction
Keywordspolymorphism
Keywordslattice energy
KeywordsCambridge Structural Database
Keywordsblind test
TitleThe seventh blind test of crystal structure prediction: structure generation methods
TypeArticle
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