通过拓引导采样和机器学习在异质催化中发现活性相
Shisheng Zheng1, Xi-Ming Zhang2, Heng-Su Liu2
1College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China. zhengss@xmu.edu.cn.
Nature communications
|March 15, 2025
概括
这项研究引入了一个新的计算框架,用于有效地发现异质催化物的活性相. 该方法使用拓学和机器学习来建模复杂的催化系统,并预测不同条件下的材料行为.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 了解活性相对于推进异质催化是至关重要的.
- 活性相的计算建模在配置生成和计算方面面临着挑战.
- 现有的方法与大量的原子配置和材料形态学作斗争.
研究的目的:
- 开发一个自动和高效的框架来探索催化剂中的活性阶段.
- 在各种协调环境和材料形态学中系统地采样配置.
- 为了使用高效的机器学习力场来实现快速计算.
主要方法:
- 使用基于拓的算法,利用持久的同质性进行系统的配置采样.
- 采用高效的机器学习力场来快速进行计算分析.
- 展示了 (Pd) 中吸收和 (Pt) 集群的氧化动态的框架.
主要成果:
- 成功模拟了Pd中的吸收,揭示了对二氧化碳电还原至关重要的"六进制"重建.
- 研究了Pt集群氧化,表明氧气的纳入减少了氧气还原反应中的活性.
- 预测的活性相及其催化影响与实验观测密切一致.
结论:
- 拟议的框架有效地模拟复杂的催化系统.
- 它可以在特定环境条件下发现活性相.
- 这一战略促进了对异质催化剂的理解和设计.
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