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在过渡金属纳米集群上优化分子描述器,可靠地进行吸附能量预测

Lucas B Pena¹, Felipe V Calderan², Priscilla Felício-Sousa³

¹Centro Federal de Educação Tecnológica de Minas Gerais, 30421-169 Belo Horizonte, MG, Brazil.

|January 26, 2026

概括

机器学习模型可以使用结构描述符预测催化活动. 添加电子功能改善了过渡金属纳米集群的模型通用性,减少了昂贵的实验研究.

科学领域:

催化剂是一种催化剂.
材料科学材料科学材料科学
计算化学计算化学

背景情况:

有效的催化过程对于将污染物转化为有价值的化学物质至关重要.
过渡金属纳米集群为催化提供可调节的特性,但需要进行广泛的研究来识别活性位点.
密度函数理论 (DFT) 的计算用于确定吸附能量,但在计算上很昂贵.

研究的目的:

评估机器学习模型在催化中的结构描述符的预测性能和可转移性.
评估不同描述符对预测吸附剂在过渡金属纳米集群上的吸附能量的影响.
探索提高机器学习模型在催化剂发现中的通用性的方法.

主要方法:

利用随机森林回归算法来训练机器学习模型.
采用库伦矩阵和多体张量表示作为结构描述符.
在各种纳米集群-吸附剂数据集上测试模型性能,包括外部,前所未有的例子.
集成的电子特征,如不配对电子的数量,以提高模型的概括性.

主要成果:

库伦矩阵和多体张量表示都在测试数据上实现了0.05 eV的平均绝对误差.
当应用到具有新型示例的外部数据集时,模型性能显著下降.
包括未配对电子的数量作为电子特征改善了模型的概括性,尽管平均绝对误差略高.

结论:

仅仅结构描述符就显示了各种催化系统的概括性的局限性.
催化剂的机器学习模型高度依赖于培训数据的质量和多样性.
将相关的电子特征与结构描述符相结合,可以增强催化模型的预测能力和可转移性.

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