Ab initio random structure searching
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It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density-functional-theory (DFT), is a rapidly growing field. Here we describe our simple, elegant and powerful approach to searching for structures with DFT, which we call ab initio random structure searching (AIRSS). Applications to discovering the structures of solids, point defects, surfaces, and clusters are reviewed. New results for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.
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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 [2]
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Cited by
Sorted by citations
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Comparing molecules and solids across structural and alchemical space
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Unsupervised machine learning in atomistic simulations, between predictions and understanding
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Application of machine learning for advanced material prediction and design
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Machine learning in materials design: Algorithm and application*
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Generative AI for crystal structures: A review
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- Authors
- Pickard, C. J., & Needs, R. J.
- Affiliations
- University College London (GB)
- University of Cambridge (GB)
- Venue
- Journal of Physics: Condensed Matter
- Publisher
- IOP Publishing
- Published
- 2011-01-05
- Volume
- 23
- Issue
- 5
- Pages
- 053201
- ISSN
- 0953-8984 (ISSN-L)
- 1361-648X
- OpenAlex
- W2139062161
- Cited by
- 1,302
- References
- 133
- Citations by year
- 2026: 78 · 2025: 115 · 2024: 105 · 2023: 103 · 2022: 88 · 2021: 136 · 2020: 172 · 2019: 78 · 2018: 91 · 2017: 80 · 2016: 63 · 2015: 59 · 2014: 57 · 2013: 35 · 2012: 28
- FWCI
- 20.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Physics Condensed Matter (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/G007489/2, EP/F032773/1)
- Topics
- 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
- Physical Sciences › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Keywords
- ab initio random structure searching, structure discovery, density functional theory, point defects, surfaces, clusters, silicon clusters, polymeric nitrogen, boron
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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