在温度下对金属表面的分子结合的计算评估: DFT 功能和神经网络潜力的准确性
Tatsuya Joutsuka1, Yoshiteru Itagaki1
1Department of Materials Science and Engineering, Ehime University, Matsuyama, Ehime, Japan.
Journal of computational chemistry
|November 21, 2025
概括
从金属表面上精确模拟分子脱落,需要仔细选择计算方法. 这项研究评估了密度函数理论 (DFT) 函数和神经网络潜力,为异质催化和表面科学研究提供了指导.
科学领域:
- 表面科学和异质催化研究.
背景情况:
- 了解金属表面上的分子吸附/溶解对于催化非常重要.
- 温度编程脱吸 (TPD) 的实验数据提供了基准.
研究的目的:
- 系统地评估密度函数理论 (DFT) 函数和神经网络潜力,用于建模分子结合.
- 将计算结果与过渡金属上的各种吸附物的实验TPD数据进行比较.
主要方法:
- 评估DFT功能 (PBE,PBE-D3,revPBE-D3,optB88-vdW,BEEF-vdW,SCAN+rVV10) 和一个神经网络的潜力.
- 用实验TPD数据对计算的散量格子常数,表面能量和结合能量进行比较.
- 利用机器学习潜力进行分子动力学模拟以提取动力学参数.
主要成果:
- 分散校正的DFT函数 (PBE-D3,SCAN+rVV10) 准确地预测了批量格子常量.
- optB88-vdW和SCAN+rVV10在表面能量方面表现良好.
- BEEF-vdW提供了更具约束力的能源协议,但较不准确的散装特性.
- 机器学习的潜能有效地重现了DFT的能量,并实现了现实的动力参数提取.
结论:
- DFT和神经网络潜力可以准确地模拟分子吸附.
- 功能选择涉及到批量,表面和结合能量的精度之间的权衡.
- 这项工作为选择模拟吸过程的计算方法提供了实际指导.
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