使用图形神经网络潜力访问数字能量hessians及其在异质催化中的应用
Brook Wander1, Joseph Musielewicz1, Raffaele Cheula1,2
1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
The journal of physical chemistry. C, Nanomaterials and interfaces
|February 26, 2025
概括
机器学习潜能准确地计算了表面中间体的赫西安,使得吉布斯的自由能量计算成为可能. 这种方法改善了过渡状态的搜索,并解释了吸附物转化.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 催化剂是一种催化剂.
背景情况:
- 对于计算吉布斯自由能量和优化过渡状态而言,获得潜在能量Hessian至关重要.
- 机器学习潜力 (MLP) 为加速这些计算提供了一个有希望的途径.
研究的目的:
- 评估预训练的开放催化剂项目 (OCP) MLPs在确定吸附中间体的赫西安时的准确性.
- 评估MLP衍生的Hessian对于Gibbs自由能量计算和过渡状态搜索的实用性.
- 为了研究吸附剂转化的贡献,超出和近似.
主要方法:
- 使用现成预训练的OCP MLPs来计算表面吸附中间体的潜在能量hessian.
- 使用MLP衍生的Hessians计算了Gibbs自由能量对振动的贡献.
- 研究了吸附剂在300 K的转化的影响.
- 应用MLP确定的Hessian信息到过渡状态搜索算法.
主要成果:
- OCP MLPs准确地确定吸附中间体的Hessian,平均绝对误差 (MAE) 为58cm-1.
- 具有偏移校正的高性能MLP模型估计了振动,MAE在300 K时为0.042 eV.
- 在随机抽样的系统中,有94%的系统在300 K时表现出转化大于0.1 eV.
- 在搜索中,MLP-Hessian信息减少了65-93%的非融合过渡状态.
结论:
- 经过预先训练的OCPMLP可以可靠地确定表面中间体的Hessian,从而实现准确的Gibbs自由能量计算.
- MLP为结合振动和转化提供了一个有价值的工具,这对于理解吸附现象至关重要.
- 使用MLP衍生的Hessian显著提高了催化中的过渡状态搜索的效率和趋同.
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