Quantification of Crystal Packing Similarity from Spherical Harmonic Transform
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918045613555712 |
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| author | Zhu, Qiang Tang, Weilun Hattori, Shinnosuke |
| author_facet | Zhu, Qiang Tang, Weilun Hattori, Shinnosuke |
| contents | In this work, we present a new computational approach to characterize and classify molecular packing in the solid states. The key idea is to project each neighboring molecule (or short contact) from the centered molecule into a unit sphere according to the interaction energy. Consequently, the similarity between two spherical images can be evaluated from the spherical harmonics expansion based on the maximum cross-correlation. We apply this approach to successfully reproduce the previous packing assignment on a small amount of data with an improved categorization. Furthermore, we conduct a packing similarity analysis over 2000 hydrocarbon crystal data sets and uncover a set of abundant packing motifs. Unlike the previous approaches based on the subjective visual comparison at the real space, our approach provides a more robust way to measure the packing similarity, thus paving the way for a rapid classification of large scale crystal data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_12548 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Quantification of Crystal Packing Similarity from Spherical Harmonic Transform Zhu, Qiang Tang, Weilun Hattori, Shinnosuke Materials Science In this work, we present a new computational approach to characterize and classify molecular packing in the solid states. The key idea is to project each neighboring molecule (or short contact) from the centered molecule into a unit sphere according to the interaction energy. Consequently, the similarity between two spherical images can be evaluated from the spherical harmonics expansion based on the maximum cross-correlation. We apply this approach to successfully reproduce the previous packing assignment on a small amount of data with an improved categorization. Furthermore, we conduct a packing similarity analysis over 2000 hydrocarbon crystal data sets and uncover a set of abundant packing motifs. Unlike the previous approaches based on the subjective visual comparison at the real space, our approach provides a more robust way to measure the packing similarity, thus paving the way for a rapid classification of large scale crystal data. |
| title | Quantification of Crystal Packing Similarity from Spherical Harmonic Transform |
| topic | Materials Science |
| url | https://arxiv.org/abs/2207.12548 |