通过机器学习方法对二维过渡金属四基的比较分析
Muhammad Irfan1, Nabeela Bashir1, AbdulGuddoos S A Gaid2
1Department of Mathematics, University of Okara, Okara, Pakistan.
Scientific reports
|January 31, 2026
概括
这项研究使用分子图形理论和机器学习来预测过渡金属四氧化 (TM-THQ) 材料的. 结果显示,拓描述器准确预测TM-THQ,集体方法优异.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 金属有机框架 (MOF),特别是过渡金属四基 (TM-THQ),因其稳定性和高表面积而受到越来越多的关注.
- 这些特性使得TM-THQ材料在二氧化碳转化等应用中具有前景.
研究的目的:
- 通过分子图理论来研究TM-THQ的结构性质.
- 通过拓指数预测TM-THQ材料的稳定性和反应性.
- 用Shannon的模型和机器学习来估计TM-THQ的.
主要方法:
- 应用分子图形理论来表示TM-THQ结构.
- 计算各种拓指数 (扎格勒布,兰迪奇,原子键连接,几何算法,和连接).
- 使用Python进行预测的对数,随机森林和XGBoost回归模型的开发和比较.
主要成果:
- 在拓描述符和TM-THQ之间建立了强大的预测关系.
- 机器学习模型,特别是集体方法 (随机森林和XGBoost),在预测方面表现出很高的准确性.
- 拓指数有效地解释了原子连接,并预测了材料的稳定性和反应性.
结论:
- TM-THQ的可以从其拓描述符中可靠地预测.
- 机器学习,特别是组合技术,为分析MOF属性提供了强大的方法.
- 这项研究为理解和优化TM-THQ材料提供了一个计算框架.
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