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化学功能化的结构和动力学:联合密度功能理论和机器学习方法

Paweł Wojciechowski¹, Andrzej Bobyk², Mariusz Krawiec¹

¹Institute of Physics, Maria Curie-Skłodowska University in Lublin, Pl. M. Curie-Skłodowskiej 1, 20-031 Lublin, Poland.

Materials (Basel, Switzerland)

|November 27, 2025

概括

在先进的电子和能源应用中,控制元素吸附剂对的相互作用是关键. 这项研究使用密度函数理论 (DFT) 和机器学习 (ML) 来预测稳定的吸附配置,加速材料发现.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
表面科学是一门学科.

背景情况:

在二维 (2D) 材料上的元素吸附剂对于电子,传感和能源应用至关重要.
是一种类似于石墨烯的异质,可为此类应用提供可调节的特性.
了解吸附剂-表面相互作用对于设计下一代设备至关重要.

研究的目的:

通过混合密度函数理论 (DFT) 和机器学习 (ML) 方法对烯进行原子吸附模型的研究.
在功能化表面上开发吸附几何和能量预测模型.
为2D材料功能化建立一个高通量选框架.

主要方法:

采用自旋极化密度函数理论 (DFT) 来优化近2000个原子吸附模型.
根据计算的吸附能量,选择了最稳定的配置.
训练有素的机器学习 (ML) 模型,包括基于树的算法和神经网络,用于预测吸附性质.

主要成果:

通过使用训练的ML模型,成功预测了吸附几何 (分类) 和吸附能量 (回归).
确定了各种元素的稳定吸附配置,并在上覆盖.
证明了混合 DFT + ML 方法在快速查中的有效性.

结论:

混合DFT + ML框架使得元素功能化的高通量选能够高效地进行.
这种方法加速了用于电子和催化应用的二维材料的发现.
为设备工程中的表面修饰策略提供可转移的方法.

关键词:

吸附吸附是一种吸附.密度函数理论密度函数理论机器学习是机器学习.烯是一种酸盐.表面功能化的功能化.

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