DOAJ Open Access 2024

Deep-learning prediction of safety moiety of salen-type complex crystals towards explosive perchlorate salts

Takashiro Akitsu Yuji Takiguchi Shintaro Suda Daisuke Nakane

Abstrak

Perchlorate compounds are well-known for their explosive and hazardous nature. Considering previously reported perchlorate crystals of salen-type manganese (III) complexes, our study aimed to identify the specific molecular/crystal structure responsible for their explosive properties. Employing deep learning, we conducted an analysis of the Hirschfeld surface for salen-type metal complexes within a crystal structure database. The results indicate that the salen-type complex site lacks distinctive structural features, attributing its explosive potential to the chemical bonding of the perchlorate ion and the surrounding intermolecular interactions.

Topik & Kata Kunci

Explosives and pyrotechnics

Penulis (4)

Takashiro Akitsu

Yuji Takiguchi

Shintaro Suda

Daisuke Nakane

Format Sitasi

APA MLA BibTeX

Akitsu, T., Takiguchi, Y., Suda, S., Nakane, D. (2024). Deep-learning prediction of safety moiety of salen-type complex crystals towards explosive perchlorate salts. https://doi.org/10.1016/j.fpc.2023.12.004

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1016/j.fpc.2023.12.004
Akses: Open Access ✓