基于图形的机器学习框架用于预测金属有机框架中的储能容量
Azzam Alfarraj1,2, Monther Rashed Alfuraidan1,2, Abdul Malik P Peedikakkal3,4
1Department of Mathematics, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia.
Journal of chemical information and modeling
|September 29, 2025
概括
机器学习使用图形理论预测金属有机框架 (MOF) 中的储存. 这种方法加速了用于清洁能源应用的先进MOF的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 是一种清洁的能源载体,但安全储存是一个重大挑战.
- 金属有机框架 (MOF) 由于其高表面积和可调节的孔隙,显示出对固态储存的希望.
研究的目的:
- 开发一种机器学习框架,用于预测MOF中的吸收量.
- 将光谱图理论与用于材料发现的数据驱动建模相结合.
主要方法:
- 以加权图形表示MOF结构.
- 提取20个基于图形的描述符 (例如,光谱特征,萨格勒布索引),捕获拓和几何属性.
- 在使用这些描述符的3300个MOF上训练回归模型 (XGBoost).
主要成果:
- 在XGBoost模型中,重度吸收的R^2为0.737 (UG),体积吸收的R^2为0.698.
- 该框架确定了与增强的储能能力相关的基于图形的关键动机.
- 该模型显示了高预测准确度 (提供RMSE和MAE值).
结论:
- 基于图形的机器学习框架提供了一个可扩展和高效的方法来预测MOF中的储存.
- 这种方法促进了反向材料设计,加速了用于储存的新型MOF的发现.
- 化学图形理论和机器学习的整合为下一代清洁能源材料铺平了道路.
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