化工学报 ›› 2021, Vol. 72 ›› Issue (12): 6262-6273.doi: 10.11949/0438-1157.20211258

• 催化、动力学与反应器 • 上一篇    下一篇

基于机器学习的二氧化碳电化学还原制备甲酸盐研究

刘文萱(),张嘉毅,陆奇,张皓晨()   

  1. 清华大学化学工程系，化学工程联合国家重点实验室，北京 100084
  • 收稿日期:2021-08-31 修回日期:2021-10-28 出版日期:2021-12-05 发布日期:2021-12-22
  • 通讯作者: 张皓晨 E-mail:liuwx21@mails.tsinghua.edu.cn;zhc17@mails.tsinghua.edu.cn
  • 作者简介:刘文萱（1999—），女，博士研究生，liuwx21@mails.tsinghua.edu.cn
  • 基金资助:
    国家重点研发计划项目(2017YFB0702800)

Investigation of electroreduction of carbon dioxide into formate based on machine learning

Wenxuan LIU(),Jiayi ZHANG,Qi LU,Haochen ZHANG()   

  1. Department of Chemical Engineering, State Key Laboratory of Chemical Engineering, Tsinghua University, Beijing 100084, China
  • Received:2021-08-31 Revised:2021-10-28 Published:2021-12-05 Online:2021-12-22
  • Contact: Haochen ZHANG E-mail:liuwx21@mails.tsinghua.edu.cn;zhc17@mails.tsinghua.edu.cn

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摘要：

系统研究了不同双金属中心催化剂催化二氧化碳电化学还原制备甲酸盐。借助机器学习，确定了反应中心金属原子序数、电负性和电离能等特征对双金属中心催化剂表面二氧化碳还原具有主要的影响。基于这些特征，通过高通量机器学习快速预测了105种双金属中心催化剂二氧化碳电还原制甲酸盐及其主要竞争反应的Gibbs自由能变，筛选出29种双金属中心催化剂更倾向于二氧化碳还原得到甲酸盐，是潜在的转化二氧化碳为甲酸盐的高性能催化材料。运用类似的方法预测了105种双金属中心催化剂表面二氧化碳还原中间体的结构，发现中间体吸附能与其吸附构型具有显著的相关关系。

关键词: 二氧化碳, 电化学, 催化剂, DFT计算, 机器学习

Abstract:

Electrocatalytic reduction of carbon dioxide to high value-added chemical products has provided a new route to alleviate greenhouse effect and other global problems, attracting intensive attention from both industry and academia. However, it still remains a great challenge to develop electrocatalysts with high performance for practical applications. As one of the major products from carbon dioxide electroreduction, formate is of key importance due to its stability, portability, and high volumetric energy density. In this work, we systematically studied the performance of electrochemical reduction of carbon dioxide to formate on various dual-metal-site catalysts using machine learning in conjunction with density functional theory (DFT) calculations. It was determined that the atomic number, electronegativity and ionization energy of metal atoms in the reaction center are major factors influencing the adsorption of formate intermediates over dual-metal-site catalysts. Based on these features, we predicted the adsorption free energy change for the electroreduction of carbon dioxide to formate and its main competitive reaction, hydrogen evolution reaction. 29 out of 105 dual-metal-site catalysts were identified as potential catalysts for formate production from carbon dioxide electroreduction. A similar method was used to predict the structure of the carbon dioxide reduction intermediates on the surface of 105 dual-metal-site catalysts, and it was found that the adsorption energy of the intermediates has a significant correlation with their adsorption configuration.

Key words: carbon dioxide, electrochemistry, catalyst, DFT calculation, machine learning

中图分类号: 

  • O 646.51

图1

石墨烯-N6-M1-M2双金属中心催化剂模型[灰色球为碳原子，淡蓝色球为氮原子，深蓝色球为金属原子（M1和M2）]"

图2

CO2ER和HER关键反应中间体的不同吸附构型[灰色球为碳原子，淡蓝色球为氮原子，白色球为氢原子，红色球为氧原子，深蓝色球为金属原子（M1和M2）]"

表1

随机抽取的23种DMSCs的ΔG*H、ΔG*HCOO和*HCOO吸附构型"

序号M1M2*HCOO吸附构型ΔG*H/eVΔG*HCOO/eV
1CuNiM1-*HCOO-M2双配位型0.430.89
2CoPdM-*HCOO单配位扭曲型-0.760.26
3RhIrM-*HCOO单配位扭曲型-0.500.51
4RhPtM-*HCOO单配位扭曲型-0.610.54
5IrCrM-*HCOO单配位型-0.32-0.43
6IrMnM-*HCOO单配位型-0.320.00
7ZnFeM1-*HCOO-M2双配位型-0.45-0.85
8RuFeM1-*HCOO-M2双配位型-0.050.12
9CrCoM1-*HCOO-M2双配位型-0.29-0.48
10MnAgM-*HCOO单配位型-0.66-0.62
11AgCoM1-*HCOO-M2双配位型0.080.27
12CrPdM-*HCOO单配位型-0.43-0.77
13NiPtM-*HCOO单配位扭曲型-0.341.31
14AgAuM-*HCOO单配位型-0.240.92
15MnPtM-*HCOO单配位型-0.61-0.53
16ZnNiM1-*HCOO-M2双配位型-0.31-0.25
17RuCuM1-*HCOO-M2双配位型-0.68-0.13
18CuAgM1-*HCOO-M2双配位型0.130.13
19NiCoM1-*HCOO-M2双配位型-0.590.53
20RuCoM1-*HCOO-M2双配位型-0.240.34
21CrMnM1-*HCOO-M2双配位型-0.18-1.21
22RhAgM-*HCOO单配位型-0.020.96
23PdZnM-*HCOO单配位型-0.72-0.38

表2

机器学习的完整52个特征"

特征定义特征定义
Z1 、Z2M1和M2的原子序数(Z1+Z2)/2平均原子序数
N1 、N2M1和M2的价电子数(N1+N2)/2平均价电子数
PE1 、PE2M1和M2的电负性[34](PE1+PE2)/2平均电负性
IE1 、IE2M1和M2的第一电离能[34](IE1+IE2)/2平均第一电离能
EA1 、EA2M1和M2的电子亲和能[35](EA1+EA2)/2平均电子亲和能
Nd1 、Nd2M1和M2的d电子数(Nd1+Nd2)/2平均d电子数
WF1 、WF2M1和M2的功函数[34](WF1+WF2)/2平均功函数
r1 、r2M1和M2的原子半径[34](r1+r2)/2平均原子半径
R1 、R2M1和M2的范德华半径[36](R1+R2)/2平均范德华半径
IE1/Nd1、IE2/Nd2第一电离能除以d电子数[(Z1+Z2)/2]2平均原子序数的平方值
EA1/Nd1、EA2/Nd2电子亲和能除以d电子数[(N1+N2)/2]2平均价电子数的平方值
PE1/Nd1、PE2/Nd2电负性除以d电子数[(PE1+PE2)/2]2平均电负性的平方值
PE1×Nd1、PE2×Nd2电负性与d电子数之积[(IE1+IE2)/2]2平均第一电离能的平方值
PE1+PE2、PE1-PE2电负性之和、电负性之差[(EA1+EA2)/2]2平均电子亲和能的平方值
r1+r2原子半径之和[(Nd1+Nd2)/2]2平均d电子数的平方值
R1+R2范德华半径之和[(WF1+WF2)/2]2平均功函数的平方值
WF1/Nd1、WF2/Nd2功函数除以d电子数[(r1+r2)/2]2平均原子半径的平方值
WF1+WF2、WF1-WF2功函数之和、功函数之差[(R1+R2)/2]2平均范德华半径的平方值

图3

ΔG*HCOO的特征筛选研究"

图4

8种算法对ΔG*HCOO预测结果的对比"

图5

8种算法预测*HCOO吸附构型的准确率"

图6

ΔG*HCOO的7个特征在训练集和全数据集下的归一化相对数值的出现频率分布情况"

图7

机器学习对ΔG*HCOO的预测结果"

图8

机器学习对ΔG*H的预测结果"

图9

机器学习对ΔG*HCOO-ΔG*H的预测结果"

图10

机器学习对*HCOO吸附构型的预测结果"

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