Multiscale Catalyst Design for Steam Methane Reforming Assisted by Deep Learning,The Journal of Physical Chemistry C

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Multiscale Catalyst Design for Steam Methane Reforming Assisted by Deep Learning
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2021-05-12 , DOI: 10.1021/acs.jpcc.1c01632
Changming Ke _{1,

2,

3} , Weigang He ₁ , Shixue Liu ₄ , Xiao Ru ₁ , Shi Liu _{2,

3,

5} , Zijing Lin ₁

Affiliation

Computational design of high-quality catalysts targeting specific operation conditions is a challenging task due to the mechanistic, structural, and environmental complexities across multiple length and time scales. A multiscale method of a catalyst design linking ab initio calculations, microkinetics, and multiphysics modeling was proposed to address this challenge. The chemistry-based analytical model derived from a microkinetic model assisted by first-principles-based deep neural networks efficiently bridged zero Kelvin ab initio microscopic descriptors and multiphysics modeling. We applied the multiscale method to the design of carbon-resistant steam methane reforming catalysts, successfully identifying a few cost-efficient bimetallic alloys for CH₄ internal reforming solid oxide fuel cells. The multiphysics modeling demonstrates that catalysts of relatively low activity such as NiZn are actually beneficial for fuel efficiency, highlighting the importance of the multiphysics model for a multiscale computational catalyst design.

中文翻译：

深度学习辅助蒸汽甲烷重整的多尺度催化剂设计

由于在多个长度和时间尺度上的机械，结构和环境复杂性，针对特定操作条件的高质量催化剂的计算设计是一项具有挑战性的任务。提出了一种从头计算，微观动力学和多物理场建模联系起来的催化剂设计的多尺度方法，以应对这一挑战。从基于第一原理的深层神经网络的微观动力学模型派生的基于化学的分析模型有效地将零开尔文从头算起的微观描述子与多物理场建模联系在一起。我们将多尺度方法应用于耐碳蒸汽甲烷重整催化剂的设计，成功地确定了一些具有成本效益的CH ₄双金属合金内部重整固体氧化物燃料电池。多物理场模型表明，活性相对较低的催化剂（例如NiZn）实际上对提高燃油效率很有帮助，突出了多物理场模型对于多尺度计算催化剂设计的重要性。

更新日期：2021-05-27

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