在Mg-Alkoxide功能化共价有机框架 (COFs) 中基于吸附的储量的计算探索:力场和机器学习模型
Yu Chen1, Guobin Zhao1, Sunghyun Yoon1
1School of Chemical Engineering, Pusan National University, Busan 46241, Republic of Korea.
ACS applied materials & interfaces
|October 30, 2024
概括
开发用于高效储存的新材料至关重要. 这项研究使用计算方法设计和选了Mg-氧化物功能化共价有机框架 (COF),确定了安全和节能储存的有希望的候选人.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 储能 储能 储能 储能 储能 储能
背景情况:
- 是一种清洁的能源载体,但高效的储存仍然是一个重大挑战.
- 目前的储存方法 (高压压缩或冷液化) 耗费大量能源且昂贵.
- 在较低的压力和温度下开发安全,可靠和节能的存储解决方案是必不可少的.
研究的目的:
- 通过计算设计和选一个大型的Mg-氧化物功能化共价有机框架 (COFs) 数据库,用于储存.
- 开发精确的计算模型来预测结能和储能.
- 为了评估功能化COF在不同温度下的重度和体积储性能.
主要方法:
- 2902 Mg-氧化物功能化COF的系统设计.
- 高通量 (HT) 计算选使用MP2计算绑定能量和拟合修改的摩尔斯力场 (FF) 参数.
- HT宏大法典蒙特卡洛 (GCMC) 模拟用于吸收计算和机器学习 (ML) 模型用于性能预测.
主要成果:
- 氧化功能化显著提高体积储能能力,特别是在较小孔的COF中.
- 开发的ML模型准确地预测了重力度 (MAE:0.061重量%) 和体积 (MAE:0.456g/L) 交付能力.
- 选发现了有希望的COF结构,用于111,231和296K的高效储存.
结论:
- 氧化功能化是一种有效的策略,可以改善COF中的储存.
- 开发的计算和机器学习方法可以快速选用于储存的新材料.
- 这项工作为设计用于安全和高效的能能源应用的先进材料提供了途径.
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