通过可解释机器学习方法预测和理解双过渡金属MXenes的工作功能
Yihao Zheng1, Xiangcui Qiu1, Haibo Li1
1Shandong Provincial Key Laboratory/Collaborative Innovation Center of Chemical Energy Storage & Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252000, China.
Langmuir : the ACS journal of surfaces and colloids
|May 13, 2025
概括
机器学习模型准确地预测了双过渡金属MXenes的工作功能. 外部金属元素显著影响工作功能,指导材料设计.
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
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- MXenes是一种有前途的2D材料类,具有可调节的电子特性.
- 了解和预测MXenes的工作功能对于其在电子设备中的应用至关重要.
- 双过渡金属MXenes为物业调整提供了更多的可能性.
研究的目的:
- 开发可解释的机器学习模型,用于预测双过渡金属MXenes的工作功能.
- 确定控制这些材料工作功能的关键元素特征.
- 为合理设计具有所需工作功能的基于MXene的材料提供框架.
主要方法:
- 使用第一原则计算,生成了242个双过渡金属MXene结构的数据集.
- 训练和评估了包括Random Forest在内的各种机器学习回归模型.
- 确定独立性选和分散操作员 (SISSO) 方法被用于导出分析模型.
主要成果:
- 随机森林模型显示出高的预测准确性 (R^2 = 0.86 ± 0.03).
- 功能重要性分析显示,外部过渡金属 (M1) 对工作功能具有主导作用.
- 来自SISSO的分析表达式实现了类似的准确性 (R^2 = 0.82 ± 0.04) 并提供了物理洞察力.
结论:
- 可解释机器学习为预测MXene工作函数提供了一种有效的方法.
- 双过渡金属MXenes的工作功能主要由外部过渡金属决定.
- 这些发现有助于加速发现和设计用于特定应用的新型MXene材料.
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