反应能量在预测化学反应障碍中的重要性与机器学习模型
Nithin Lalith1, Aayush R Singh2, Joseph A Gauthier1
1Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA.
概括
机器学习模型可以在没有反应能量的情况下预测催化激活障碍. 包括反应能量在内,可以提高模型性能和对 CO2 减排等复杂反应的概括性.
科学领域:
- 计算化学和催化剂的应用
- 材料科学是一种材料科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 了解异质催化过程需要电子结构模拟和微动力学模型.
- 通过位点检索计算激活障碍是复杂反应网络的瓶.
- 布伦斯特德-埃文斯-波利亚尼 (BEP) 缩放模型的范围有限,仍然需要反应能量计算.
研究的目的:
- 研究机器学习 (ML) 模型来预测激活障碍,而无需先前了解反应能量.
- 评估包括反应能量的对ML模型性能和通用性的影响.
- 探索复杂反应的可行性,如电化学二氧化碳减排和废水整治.
主要方法:
- 在超过500个脱激活障碍数据库上训练了线性和非线性ML模型.
- 评估模型性能,并没有反应能量作为一个特征.
- 评估了超出培训集的新催化系统的可通用性.
主要成果:
- 非线性ML模型实现了与BEP缩放可比的性能,用于预测新系统上的激活障碍,即使没有反应能量.
- 包括反应能量显著改善了线性和非线性ML模型.
- 反应能量的包含大大提高了对训练数据之外的系统的模型通用性.
结论:
- 反应能量是开发催化激活障碍的准确预测模型的关键特征.
- 机器学习为克服催化研究中的计算瓶提供了一条有希望的途径.
- 对反应能量的可靠预测对于推进复杂的催化系统建模至关重要.
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