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虚拟数据库构建和机器学习辅助的高吞吐量评估无形多孔碳材料作为吸附剂

Yuqing Qiu¹, Zhiyuan Zhang¹, Zhen-Wu Shao¹

¹School of Chemical Engineering, Sichuan University, Chengdu 610065, China.

ACS applied materials & interfaces

|March 3, 2025

概括

研究人员开发了一种方法来选无形多孔碳 (APC) 材料以检测-125 (I2) 吸附. 机器学习确定了诸如表面积和毛孔大小等关键特征,加速了APC用于核应用的开发.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
核工程核工程是指核工程.

背景情况:

有效的-125 (I2) 吸附对于核工业应用至关重要.
无形多孔碳 (APC) 材料显示出作为I2吸附剂的前景.
需要高通量选方法来确定最佳的APC结构.

研究的目的:

开发一种全面的方法,用于APC材料的高通量选和分析,用于I2吸收.
建立APC模型的虚拟数据库,并评估它们的I2吸附能力.
使用机器学习识别影响APC性能的关键结构和化学特征.

主要方法:

使用液体火分子动力学模拟生成一个包含19,599个APC模型的虚拟数据库.
大规模的大法典蒙特卡罗模拟以确定不同度的I2吸附能力.
机器学习和夏普利增材扩张 (SHAP) 分析,以将材料特征与吸附行为相关联.

主要成果:

确定了影响APC发展的因素,包括表面积和毛孔大小分布,这些因素因I2度而异.
为采样的 APC 生成了一系列 I2 吸附能力.
建立了一个框架,以理解APC材料中的结构-属性关系.

结论:

开发的方法加速了用于I2吸附的APC材料的选和开发.
提供基本数据库和研究框架,以提高对用于核应用的APC材料的理解.
确定了影响I2吸附的关键材料特征,指导了未来的材料设计.

关键词:

无形多孔碳是一种无形的多孔碳.吸附吸附是由于的吸附.机器学习是机器学习.分子模拟分子模拟虚拟材料的生成是虚拟材料的生成.

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