The phase-seeding method for solving non-centrosymmetric crystal structures: a challenge for artificial intelligence Article Swipe
Benedetta Carrozzini
,
Liberato De
,
Cinzia Giannini
,
Angela Altomare
,
Rocco Caliandro
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1107/s2053273325002797
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1107/s2053273325002797
The overall crystallographic process involves acquiring experimental data and using crystallographic software to find the structure solution. Unfortunately, while diffracted intensities can be measured, the corresponding phases – needed to determine atomic positions – remain experimentally inaccessible (phase problem). Direct methods and the Patterson approach have been successful in solving crystal structures but face limitations with large structures or low-resolution data. Current artificial intelligence (AI) based approaches, such as those recently developed by Larsen et al. [ Science (2024), 385 , 522–528], have been applied with success to solve centrosymmetric structures, where the phase is binary (0 or π). The current work proposes a new phasing method designed for AI integration, applicable also to non-centrosymmetric structures, where the phase is a continuous variable. The approach involves discretizing the initial phase values for non-centrosymmetric structures into a few distinct values ( e.g. values corresponding to the four quadrants). This reduces the complex phase problem from a continuous regression task to a multi-class classification problem, where only a few phase seed values need to be determined. This discretization allows the use of a smaller training dataset for deep learning models, reducing computational complexity. Our feasibility study results show that this method can effectively solve small, medium and large structures, with the minimum percentage of phase seeds (three or four points in the interval [0, 2π]), and 10% to 30% of seed symmetry-independent reflections. This phase-seeding method has the potential to extend AI-based approaches to solve crystal structures ab initio , regardless of complexity or symmetry, by combining AI classification algorithms with classical phasing procedures.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1107/s2053273325002797
- https://journals.iucr.org/a/issues/2025/03/00/lu5043/lu5043.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409525749
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409525749Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1107/s2053273325002797Digital Object Identifier
- Title
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The phase-seeding method for solving non-centrosymmetric crystal structures: a challenge for artificial intelligenceWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-17Full publication date if available
- Authors
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Benedetta Carrozzini, Liberato De, Cinzia Giannini, Angela Altomare, Rocco CaliandroList of authors in order
- Landing page
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https://doi.org/10.1107/s2053273325002797Publisher landing page
- PDF URL
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https://journals.iucr.org/a/issues/2025/03/00/lu5043/lu5043.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://journals.iucr.org/a/issues/2025/03/00/lu5043/lu5043.pdfDirect OA link when available
- Concepts
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Discretization, Phase (matter), Artificial intelligence, Computer science, Algorithm, Symmetry (geometry), Phaser, Diffraction, Mathematics, Physics, Geometry, Optics, Mathematical analysis, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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