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

doi:10.11949/0438-1157.20211258

Abstract

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

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

CLC Number:

O 646.51

Wenxuan LIU, Jiayi ZHANG, Qi LU, Haochen ZHANG. Investigation of electroreduction of carbon dioxide into formate based on machine learning[J]. CIESC Journal, 2021, 72(12): 6262-6273.

刘文萱, 张嘉毅, 陆奇, 张皓晨. 基于机器学习的二氧化碳电化学还原制备甲酸盐研究[J]. 化工学报, 2021, 72(12): 6262-6273.

Figures/Tables 12

References 36

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序号	M₁	M₂	*HCOO吸附构型	ΔG_*H/eV	ΔG_*HCOO/eV
1	Cu	Ni	M₁-*HCOO-M₂双配位型	0.43	0.89
2	Co	Pd	M-*HCOO单配位扭曲型	-0.76	0.26
3	Rh	Ir	M-*HCOO单配位扭曲型	-0.50	0.51
4	Rh	Pt	M-*HCOO单配位扭曲型	-0.61	0.54
5	Ir	Cr	M-*HCOO单配位型	-0.32	-0.43
6	Ir	Mn	M-*HCOO单配位型	-0.32	0.00
7	Zn	Fe	M₁-*HCOO-M₂双配位型	-0.45	-0.85
8	Ru	Fe	M₁-*HCOO-M₂双配位型	-0.05	0.12
9	Cr	Co	M₁-*HCOO-M₂双配位型	-0.29	-0.48
10	Mn	Ag	M-*HCOO单配位型	-0.66	-0.62
11	Ag	Co	M₁-*HCOO-M₂双配位型	0.08	0.27
12	Cr	Pd	M-*HCOO单配位型	-0.43	-0.77
13	Ni	Pt	M-*HCOO单配位扭曲型	-0.34	1.31
14	Ag	Au	M-*HCOO单配位型	-0.24	0.92
15	Mn	Pt	M-*HCOO单配位型	-0.61	-0.53
16	Zn	Ni	M₁-*HCOO-M₂双配位型	-0.31	-0.25
17	Ru	Cu	M₁-*HCOO-M₂双配位型	-0.68	-0.13
18	Cu	Ag	M₁-*HCOO-M₂双配位型	0.13	0.13
19	Ni	Co	M₁-*HCOO-M₂双配位型	-0.59	0.53
20	Ru	Co	M₁-*HCOO-M₂双配位型	-0.24	0.34
21	Cr	Mn	M₁-*HCOO-M₂双配位型	-0.18	-1.21
22	Rh	Ag	M-*HCOO单配位型	-0.02	0.96
23	Pd	Zn	M-*HCOO单配位型	-0.72	-0.38

序号	M₁	M₂	*HCOO吸附构型	ΔG_*H/eV	ΔG_*HCOO/eV
1	Cu	Ni	M₁-*HCOO-M₂双配位型	0.43	0.89
2	Co	Pd	M-*HCOO单配位扭曲型	-0.76	0.26
3	Rh	Ir	M-*HCOO单配位扭曲型	-0.50	0.51
4	Rh	Pt	M-*HCOO单配位扭曲型	-0.61	0.54
5	Ir	Cr	M-*HCOO单配位型	-0.32	-0.43
6	Ir	Mn	M-*HCOO单配位型	-0.32	0.00
7	Zn	Fe	M₁-*HCOO-M₂双配位型	-0.45	-0.85
8	Ru	Fe	M₁-*HCOO-M₂双配位型	-0.05	0.12
9	Cr	Co	M₁-*HCOO-M₂双配位型	-0.29	-0.48
10	Mn	Ag	M-*HCOO单配位型	-0.66	-0.62
11	Ag	Co	M₁-*HCOO-M₂双配位型	0.08	0.27
12	Cr	Pd	M-*HCOO单配位型	-0.43	-0.77
13	Ni	Pt	M-*HCOO单配位扭曲型	-0.34	1.31
14	Ag	Au	M-*HCOO单配位型	-0.24	0.92
15	Mn	Pt	M-*HCOO单配位型	-0.61	-0.53
16	Zn	Ni	M₁-*HCOO-M₂双配位型	-0.31	-0.25
17	Ru	Cu	M₁-*HCOO-M₂双配位型	-0.68	-0.13
18	Cu	Ag	M₁-*HCOO-M₂双配位型	0.13	0.13
19	Ni	Co	M₁-*HCOO-M₂双配位型	-0.59	0.53
20	Ru	Co	M₁-*HCOO-M₂双配位型	-0.24	0.34
21	Cr	Mn	M₁-*HCOO-M₂双配位型	-0.18	-1.21
22	Rh	Ag	M-*HCOO单配位型	-0.02	0.96
23	Pd	Zn	M-*HCOO单配位型	-0.72	-0.38

特征	定义	特征	定义
Z₁ 、Z₂	M₁和M₂的原子序数	(Z₁+Z₂)/2	平均原子序数
N₁ 、N₂	M₁和M₂的价电子数	(N₁+N₂)/2	平均价电子数
PE₁ 、PE₂	M₁和M₂的电负性^[34]	(PE₁+PE₂)/2	平均电负性
IE₁ 、IE₂	M₁和M₂的第一电离能^[34]	(IE₁+IE₂)/2	平均第一电离能
EA₁ 、EA₂	M₁和M₂的电子亲和能^[35]	(EA₁+EA₂)/2	平均电子亲和能
Nd₁ 、Nd₂	M₁和M₂的d电子数	(Nd₁+Nd₂)/2	平均d电子数
WF₁ 、WF₂	M₁和M₂的功函数^[34]	(WF₁+WF₂)/2	平均功函数
r₁ 、r₂	M₁和M₂的原子半径^[34]	(r₁+r₂)/2	平均原子半径
R₁ 、R₂	M₁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

特征	定义	特征	定义
Z₁ 、Z₂	M₁和M₂的原子序数	(Z₁+Z₂)/2	平均原子序数
N₁ 、N₂	M₁和M₂的价电子数	(N₁+N₂)/2	平均价电子数
PE₁ 、PE₂	M₁和M₂的电负性^[34]	(PE₁+PE₂)/2	平均电负性
IE₁ 、IE₂	M₁和M₂的第一电离能^[34]	(IE₁+IE₂)/2	平均第一电离能
EA₁ 、EA₂	M₁和M₂的电子亲和能^[35]	(EA₁+EA₂)/2	平均电子亲和能
Nd₁ 、Nd₂	M₁和M₂的d电子数	(Nd₁+Nd₂)/2	平均d电子数
WF₁ 、WF₂	M₁和M₂的功函数^[34]	(WF₁+WF₂)/2	平均功函数
r₁ 、r₂	M₁和M₂的原子半径^[34]	(r₁+r₂)/2	平均原子半径
R₁ 、R₂	M₁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

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