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

Takashiro Akitsu, Yuji Takiguchi, Shintaro Suda, Daisuke Nakane
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Abstract

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.

Abstract Image

深度学习预测沙仑型复合物晶体对爆炸性高氯酸盐的安全分子
众所周知,高氯酸盐化合物具有爆炸性和危险性。考虑到之前报道的高氯酸盐晶体中的柳烯类锰 (III) 复合物,我们的研究旨在确定导致其爆炸特性的特定分子/晶体结构。通过深度学习,我们在晶体结构数据库中对沙仑型金属配合物的赫希菲尔德表面进行了分析。结果表明,沙仑型复合物位点缺乏明显的结构特征,其爆炸潜力归因于高氯酸盐离子的化学键和周围的分子间相互作用。
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CiteScore
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