Crystal structure prediction using ab initio evolutionary techniques: Principles and applications
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We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input. Extremely high (nearly 100%) success rate has been observed in a few tens of tests done so far, including ionic, covalent, metallic, and molecular structures with up to 40 atoms in the unit cell. We have been able to resolve some important problems in high-pressure crystallography and report a number of new high-pressure crystal structures (stable phases: ε-oxygen, new phase of sulphur, new metastable phases of carbon, sulphur and nitrogen, stable and metastable phases of CaCO3). Physical reasons for the success of this methodology are discussed.
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The problem of obtaining a complete yet concise description of a molecular or condensed-phase structure is central to many modelling techniques.
“The problem of obtaining a complete yet concise description of a molecular or condensed-phase structure is central to many modelling techniques, including the sampling of rare events 15 and the search for stable structures 2,16,17 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [17]
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- Authors
- Oganov, A. R., & Glass, C. W.
- Affiliations
- ETH Zurich Laboratory of Crystallography, Department of Materials, , HCI G 515, Wolfgang-Pauli-Strasse 10, CH-8093 Zurich, Switzerland
- ETH Zurich (CH)
- ORCID
- Glass, Colin W.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2006-06-28
- Volume
- 124
- Issue
- 24
- Pages
- 244704-244704
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2024330948
- Cited by
- 2,596
- References
- 79
- Citations by year
- 2026: 121 · 2025: 164 · 2024: 189 · 2023: 177 · 2022: 199 · 2021: 208 · 2020: 261 · 2019: 175 · 2018: 199 · 2017: 156 · 2016: 135 · 2015: 137 · 2014: 107 · 2013: 71 · 2012: 65
- FWCI
- 32.19
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- The Journal of Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (111847, 200021-111847/1, 200021)
- Eidgenössische Technische Hochschule Zürich
- China Scholarship Council
- Topics
- Physical Sciences › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- crystal structure prediction, evolutionary algorithms, ab initio total energy calculations, metastable phases, high-pressure crystal structure, high-pressure carbon, high-pressure nitrogen, phase stability
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