基于机器学习的预测,用于金属表面化学反应的激活能量
Daniel J Hutton1, Kari E Cordes1, Carine Michel2
1Department of Biosystems Engineering, The University of Arizona, 1177 E. Fourth St., Tucson, Arizona 85719, United States.
Journal of chemical information and modeling
|September 18, 2023
概括
本研究引入了一种机器学习模型,用于预测表面催化反应的激活能量. 该方法加速了对过渡金属表面复杂化学反应的理解.
科学领域:
- 计算化学计算化学
- 表面催化剂表面催化剂
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 在计算表面催化中计算激活能量是计算上昂贵的.
- 这种费用限制了复杂反应网络的探索和催化剂选.
- 准确预测反应能量对于推进催化剂设计至关重要.
研究的目的:
- 开发一种基于机器学习的通用方法来预测激活能量.
- 为了克服与明确激活能量计算相关的计算成本.
- 为了使大型反应网络的有效探索和高通量催化剂选.
主要方法:
- 利用了一般化的布朗斯特德-埃文斯-波兰尼关系.
- 采用基于机器学习的多参数回归技术.
- 在亚利桑那大学反应数据库中的反应上训练模型.
主要成果:
- 当反应能量已知时,对于激活能达到0.14 eV的平均绝对误差 (MAE).
- 当反应能量未知时,对于激活能达到0.19 eV的MAE.
- 与明确计算相比,计算成本显著降低.
结论:
- 开发的机器学习方法提供了对激活能量的准确预测.
- 预计这种方法将取代探索复杂表面催化物的明确计算.
- 有助于有效选催化剂的所需性质和理解反应途径.
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