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

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol<sup>-1</sup> at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

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Journal
Acta Crystallographica Section B Structural Science Crystal Engineering and Materials
Published
2024-10-17
DOI
https://doi.org/10.1107/s2052520624008679
Citations
60
Primary Topic
X-ray Diffraction in Crystallography
Type
article
Field-Weighted Citation Impact
7.03

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

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60 citations
Acta Crystallographica Section B Structural Science Crystal Engineering and Materials
X-ray Diffraction in Crystallography
7.03
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The seventh blind test of crystal structure prediction: structure ranking methods

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article en
60 citations

Abstract

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol<sup>-1</sup> at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Acta Crystallographica Section B Structural Science Crystal Engineering and MaterialsVol. 80(6)
University of California, Riverside (US), Yamagata University (JP), Roche (Switzerland) (CH), Skolkovo Institute of Science and Technology (RU), University of Wyoming (US), Novartis (Switzerland) (CH), Dalhousie University (CA), Toyohashi University of Technology (JP), University of Kentucky (US), Loughborough University (GB), University of Graz (AT), Universidad de Oviedo (ES), Khalifa University of Science and Technology (AE), University of Luxembourg (LU), Universität Innsbruck (AT), Utrecht University (NL), Charles University (CZ), New York University Shanghai (CN), University of South Florida (US), AbbVie (United States) (US), Hoshi University (JP), Courant Institute of Mathematical Sciences (US), University of Hyderabad (IN), University of Oxford (GB), Syngenta (United Kingdom) (GB), Cambridge Crystallographic Data Centre (GB), Waukegan Public Library (US), Institute of Chemical and Engineering Sciences (SG), Ruđer Bošković Institute (HR), Catalent (United States) (US), Open Source Science Project (US), University of Southampton (GB), University College London (GB), New York University (US), University of Reading (GB), Ebara (Japan) (JP), Carnegie Mellon University (US), Freie Universität Berlin (DE), Newcastle University (GB), University of Silesia in Katowice (PL), University of Delaware (US), Ferdowsi University of Mashhad (IR), University of Chemistry and Technology, Prague (CZ), The University of Osaka (JP)
National Science Foundation, U.S. Department of Energy, Eli Lilly and Company, Oak Ridge Associated Universities, Dell EMC, XtalPi, University of Southampton, European Commission, University of Reading, Deutsche Forschungsgemeinschaft, Grantová Agentura České Republiky, Khalifa University of Science, Technology and Research, Hrvatska Zaklada za Znanost, Russian Science Foundation, Fundación para el Fomento en Asturias de la Investigación Científica Aplicada y la Tecnología, Karl-Franzens-Universität Graz, Bill and Carol Fox Center for Humanistic Inquiry, Emory University, Office of Science, Oak Ridge Institute for Science and Education, Natural Sciences and Engineering Research Council of Canada, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council, Japan Society for the Promotion of Science, Agencia Estatal de Investigación, Research Institute for Information Technology, Kyushu University, Division of Materials Research, Division of Chemistry, Workforce Development for Teachers and Scientists, Army Research Office, Argonne National Laboratory, Army Research Laboratory
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X-ray Diffraction in Crystallography
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