一个机器学习驱动的孔尺度网络模型 合反应动力学和粒子间传输用于催化过程设计
Ming-Liang Qu1,2,3, Zhao-Bin Ding4, Dingyue Zhang5
1State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 3, 2025
概括
通过机器学习增强的动力学 (DNMK) 的新双网络模型,有效地模拟了多孔材料中的催化反应. 这种方法加速了模拟,优化了化学过程的催化剂设计和反应器性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 催化科学 催化科学
- 计算化学的计算化学
背景情况:
- 毛孔系统中的催化过程涉及微动力学和运输现象之间的复杂相互作用.
- 准确的建模需要跨越不同的空间和时间尺度,这带来了重大的计算挑战.
- 现有的方法往往难以捕捉控制明显催化性能的复杂相互作用.
研究的目的:
- 开发一个高效的多尺度建模框架,用于反应运输合的催化过程.
- 整合机器学习以加快微动力学建模在双网络方法.
- 为反应堆优化提供关于催化剂排列和运输限制的机械洞察力.
主要方法:
- 开发一个带动力学 (DNMK) 的孔尺度双网络模型.
- 集成基于机器学习 (ML) 的替代品来加速微动力学模块.
- 应用和验证DNMK框架用于缩增强CO2化成甲醇.
主要成果:
- 与传统方法相比,实现了高达750倍的计算速度.
- 在多尺度建模中保持完整的物理和化学真实性.
- 确定了最佳的催化剂-吸收剂配置,以提高表面活性和反应器性能.
结论:
- DNMK提供了一个高分辨率的,ML驱动的平台,用于数字催化实验.
- 该框架使催化剂扩展,利用和过程加强的预测性,in silico优化成为可能.
- DNMK减少了对实验试验的依赖,为数据驱动反应堆设计铺平了道路.
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