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图像对分子的基准测试数据集具有碎形模式和层次形态识别等级

Daria M Arkhipova¹, Daniil A Boiko¹, Aleksandr A Oganov¹

¹N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, 119991, Russia.

Scientific data

|March 2, 2026

概括

在四级盐 (QPSs) 中观察到高的形态多样性. 它们的分子结构决定了微观结构,为材料设计中的机器学习提供了洞察力.

科学领域:

材料科学材料科学材料科学
化学化学化学
数据科学数据科学数据科学

背景情况:

四级盐 (QPSs) 呈现出显著的形态多样性.
这种多样性与它们的分子结构的微妙变化有关,特别是单一的甲基.
了解这种分子形态关系对于材料设计至关重要.

研究的目的:

用先进的显微镜技术记录和分析19个同源QPS的形态多样性.
为QPSs创建显微镜图像的开放访问数据集.
为机器学习应用在弥合分子形态差距方面建立一个基准.

主要方法:

使用扫描电子显微镜 (SEM) 和光学显微镜在各种放大.
分析了结晶QPS滴的微观结构.
收集并策划了19个同类QPS的显微镜图像的数据集.

主要成果:

在QPS微观结构中观察并记录了高的形态多样性.
建立了QPS分子结构和观察到的微观结构模式之间的相关性.
在QPS微观结构中确定了层次模式和碎形元素.
数据集包括显微镜图像和相应的分子信息.

结论:

QPSs的分子结构直接影响它们的结晶滴状微观结构.
生成的数据集对双向机器学习有价值:从图像中预测分子公式,反之亦然.
这项工作通过弥合分子形态差距为数据驱动材料设计提供了基础.

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