基于DFT和机器学习的Pt单原子合金电催化剂的理性设计: 应用于NO至NH3的转化

doi:10.1016/S1872-2067(24)60078-1

催化学报 ›› 2024, Vol. 62: 243-253.DOI: 10.1016/S1872-2067(24)60078-1

基于DFT和机器学习的Pt单原子合金电催化剂的理性设计: 应用于NO至NH₃的转化

刘洁宇^a, 郭海强^a, 熊钰林^a, 陈星^b, 于一夫^b^,^*(), 王长洪^a^,^*()

^a河北师范大学中燃工学院, 河北省信息融合与智能控制重点实验室, 河北石家庄 050024
^b天津大学理学院, 分子+研究院, 天津 300072

收稿日期:2024-04-01 接受日期:2024-06-04 出版日期:2024-07-18 发布日期:2024-07-10
通讯作者: *电子邮箱: yyu@tju.edu.cn (于一夫), chwang@hebtu.edu.cn (王长洪).
基金资助:
河北自然科学基金(B2022205013);河北自然科学基金(B2022205029)

Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH₃ conversion by density functional theory and machine learning

Jieyu Liu^a, Haiqiang Guo^a, Yulin Xiong^a, Xing Chen^b, Yifu Yu^b^,^*(), Changhong Wang^a^,^*()

^aCollege of Engineering, Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control, Hebei Normal University, Shijiazhuang 050024, Hebei, China
^bInstitute of Molecular Plus, School of Science, Tianjin University, Tianjin 300072, China

Received:2024-04-01 Accepted:2024-06-04 Online:2024-07-18 Published:2024-07-10
Contact: *E-mail: yyu@tju.edu.cn (Y. Yu), chwang@hebtu.edu.cn (C. Wang).
Supported by:
HeBei Natural Science Foundation(B2022205013);HeBei Natural Science Foundation(B2022205029)

摘要/Abstract

摘要：

电催化NO还原反应(NORR)合成氨, 作为一项新兴技术, 旨在减少NO污染并生产高附加值产品. 因此, 寻找合适的NORR电催化剂至关重要. 单原子合金催化剂(SAACs)因其独特的催化性能和明确的成键环境, 是研究催化剂结构-活性关系的理想对象.

本文依托密度泛函理论(DFT)计算和机器学习(ML)算法, 深入研究了Pt掺杂的SAACs在NORR合成氨反应中的催化机制, 并提出了SAACs的理性设计原则. 通过ML算法, 成功识别了与催化活性密切相关的原子和电子特征, 并定量分析了这些特征之间的协同作用. DFT计算和ML算法的联合运用不仅加深了对复杂NORR机理的理解, 还为理性设计具有特定活性中心的高效SAACs提供了一种切实可行的策略. 本文研究了13种Pt掺杂的SAACs (Pt/TM, TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ru, Rh和Pd)和14种纯过渡金属(包括Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ru, Rh, Pd和Pt)在NORR合成氨反应中的催化机制. 首先, 考察了Pt/TM的热力学稳定性和在电化学条件下的稳定性. 结果表明, Pt/TM SAACs展现出了良好的热力学稳定性. 在电化学条件下, 当外加电压等于极限电势时, 单质Zn在pH > 6时稳定存在, 而其他纯金属和Pt/TM SAACs则在整个pH范围内均能保持稳定. 随后, 探讨了这些催化剂对NO的吸附及活化能力. DFT计算发现, NO能够在所有催化剂表面稳定吸附, 并且吸附后N‒O键长与NO得电子数之间存在显著的线性相关性, 这两者可共同作为NO活化的重要指标. 接着, 考察了NORR的反应机理, 并筛选出7种具有较高过电势(U_L > ‒0.25 V)的催化剂, 包括Cu, Zn, Rh, Pt以及Pt/Cu, Pt/Zn, Pt/Rh SAACs. 考虑到H₂和N₂副产物对氨选择性的影响, 排除了Zn和Pt/Zn作为潜在的优秀催化剂. 在此基础上, 在排除贵金属 (Rh, Pt和Pt/Rh)基底的SAACs后, 筛选出Cu和Pt/Cu作为NORR合成氨反应的高效催化剂. 最后, 采用随机森林(RF)和基于压缩感知原理的数据驱动(SISSO)方法分析了SAACs的内在特性与NORR性能之间的关系. RF模型所预测的极限电势与DFT计算结果高度吻合, 模型的决定系数(R²)高达0.97, 均方根误差(RMSE)仅为0.12 V. 此外, 利用SISSO算法建立了SAACs的关键特征(如基底金属的第一电离能、NO得电子数和基底金属的d轨道电子数)与在最佳反应路径上的极限电势之间的紧密联系. 这一研究不仅加深了对NORR合成氨机制的理解, 还为高效催化剂的理性设计提供了有力的理论支持.

综上所述, 本文基于DFT计算和ML算法, 系统研究了Pt/TM SAACs在电催化还原NO合成氨过程中的催化性能. 通过筛选, Cu和Pt/Cu被确定为优秀的NORR合成氨催化剂. ML算法所预测的极限电势与DFT计算结果高度一致. 本文的研究结果为NORR电催化合成氨单原子合金催化剂的理性设计和高效筛选提供了参考.

关键词: NO还原反应, 合成氨, 单原子合金催化剂, 机器学习, 密度泛函理论

Abstract:

Electrochemical NO reduction reaction (NORR) toward NH₃ synthesis emerges as a promising approach to eliminate NO pollution and generate high-value-added products simultaneously. Therefore, exploring suitable NORR electrocatalysts is of great importance. Here, we present a design principle to evaluate the activity of single-atom alloy catalysts (SAACs), whose excellent catalytic performance and well-defined bonding environments make them suitable candidates for studying structure-activity relationships. The machine learning (ML) algorithm is chosen to unveil the underlying physics and chemistry. The results indicate that the catalytic activity of SAACs is highly correlated with the local environment of the active center, that is, the atomic and electronic features. The coeffect of these features is quantitatively verified by adopting a data-driven method. The combination of density functional theory (DFT) and ML investigations not only provides an understanding of the complex NORR mechanisms but also offers a strategy to design highly efficient SAACs with specific active centers rationally.

Key words: NO reduction reaction, Ammonia synthesis, Single-atom alloy catalyst, Machine learning, Density functional theory

刘洁宇, 郭海强, 熊钰林, 陈星, 于一夫, 王长洪. 基于DFT和机器学习的Pt单原子合金电催化剂的理性设计: 应用于NO至NH₃的转化[J]. 催化学报, 2024, 62: 243-253.

Jieyu Liu, Haiqiang Guo, Yulin Xiong, Xing Chen, Yifu Yu, Changhong Wang. Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH₃ conversion by density functional theory and machine learning[J]. Chinese Journal of Catalysis, 2024, 62: 243-253.

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链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(24)60078-1

https://www.cjcatal.com/CN/Y2024/V62/I7/243

图/表 5

Fig. 1. (a) Side view of the structural prototype of Pt/TM catalysts is shown on the left. All TM elements considered are shown in blue on the right. (b) Five-step screening method for selecting promising Pt/TM catalysts for NORR to NH3. (c) The stability of different Pt/TM SAACs, Eb represents the metal binding energy, Ec represents the metal cohesive energy. (d) The charge accumulation of Pt in Pt/TM SAACs and the electronegativity of TMs.

Fig. 2. (a) Different adsorption configurations of NO on the catalyst. (b) The adsorption free energy of NO, atomic H and NH3, respectively. (c) Differential charge density of Pt/Cu catalyst with an isosurface of 0.005 e ?-3. The charge accumulation and depletion are depicted by yellow and cyan, respectively. (d) Correspondence between the amount of NO charge transfer and the length of the N-O bond. (e) The partial density of states (PDOS) of Pt/Cu-total and NO-2p states before and after NO adsorption on Pt/Cu. The Fermi level is set to be 0 eV. (f) Projected crystal orbital Hamilton population (-pCOHP) for N-O interaction of free NO molecule and NO on Pt/Cu. (g) Linear relationship between NO charge transfer and integrated COHP (ICOHP). (h-j) Band structures of Pt/Cu, Ni, and Zn. The grey lines show the total contribution of the system. Orange, green, and blue points represent the contribution of NO’s 2p orbitals, TMs’ s and d orbitals, respectively. The point size denotes the contribution ratio. Black dash lines denote the Fermi level.

Fig. 3. (a) Schematic diagram of 6 NORR reaction pathways. (b) Heat map of reaction Gibbs free energy for each elementary step of the optimal path of catalysts.

Fig. 4. (a) The NORR limiting potentials of catalysts. (b) Reaction Gibbs free energy diagrams of Cu and Pt/Cu catalysts. Their limiting potentials are 0.03 and -0.06 V, respectively. (c) Comparison between HER limiting potentials and NORR limiting potentials of catalysts.

Fig. 5. (a) Schematic of input data acquisition and ML progress. (b) Heat map inferred by Pearson’s correlation coefficient (p). (c) Comparison between DFT and ML predicted limiting potential UL. (d) Feature importance analysis. (e) Comparison between DFT and SISSO predicted limiting potential UL.

参考文献

[1]	S. Mukherjee, S. V. Devaguptapu, A. Sviripa, C. R. F. Lund, G. Wu, Appl. Catal. B, 2018, 226, 162-181.
[2]	L. Ouyang, J. Liang, Y. Luo, D. Zheng, S. Sun, Q. Liu, M. S. Hamdy, X. Sun, B. Ying, Chin. J. Catal., 2023, 50, 6-44.
[3]	C. Li, T. Wang, J. Gong, Trans. Tianjin Univ., 2020, 26, 67-91.
[4]	V. Rosca, M. Duca, M. T. de Groot, M. T. M. Koper, Chem. Rev., 2009, 109, 2209-2244.
[5]	X. Liu, Y. Jiao, Y. Zheng, S.-Z. Qiao, ACS Catal., 2020, 10, 1847-1854.
[6]	P. Li, Z. Zhou, Q. Wang, M. Guo, S. Chen, J. Low, R. Long, W. Liu, P. Ding, Y. Wu, Y. Xiong, J. Am. Chem. Soc., 2020, 142, 12430-12439.
[7]	S. Z. Andersen, V. Čolić, S. Yang, J. A. Schwalbe, A. C. Nielander, J. M. McEnaney, K. Enemark-Rasmussen, J. G. Baker, A. R. Singh, B. A. Rohr, M. J. Statt, S. J. Blair, S. Mezzavilla, J. Kibsgaard, P. C. K. Vesborg, M. Cargnello, S. F. Bent, T. F. Jaramillo, I. E. L. Stephens, J. K. Nørskov, I. Chorkendorff, Nature, 2019, 570, 504-508.
[8]	D. F. Swearer, N. R. Knowles, H. O. Everitt, N. J. Halas, ACS Energy Lett., 2019, 4, 1505-1512.
[9]	J. Shao, H. Jing, P. Wei, X. Fu, L. Pang, Y. Song, K. Ye, M. Li, L. Jiang, J. Ma, R. Li, R. Si, Z. Peng, G. Wang, J. Xiao, Nat. Energy, 2023, 8, 1273-1283.
[10]	Y. Yang, W. Zhang, X. Tan, K. Jiang, S. Zhai, Z. Li, Coord. Chem. Rev., 2023, 489, 215196.
[11]	Y. Li, C. Cheng, S. Han, Y. Huang, X. Du, B. Zhang, Y. Yu, ACS Energy Lett., 2022, 7, 1187-1194.
[12]	R. Li, W. Ma, Y. Liu, L. Zhang, Z. Zhou, J. Mater. Chem. A, 2023, 11, 18626-18645.
[13]	J. Zhao, Z. Chen, J. Am. Chem. Soc., 2017, 139, 12480-12487.
[14]	S. Cheon, W. J. Kim, D.Y. Kim, Y. Kwon, J.-I. Han, ACS Energy Lett., 2022, 7, 958-965.
[15]	H. Jing, J. Long, H. Li, X. Fu, J. Xiao, Chin. J. Catal., 2023, 48, 205-213.
[16]	D. Meng, W. Zhan, Y. Guo, Y. Guo, L. Wang, G. Lu, ACS Catal., 2015, 5, 5973-5983.
[17]	L. Yang, Y. Liu, R. Zhang, W. Li, P. Li, X. Wang, Y. Zhou, Chin. J. Catal., 2018, 39, 646-653.
[18]	Y. Wang, Y. Qin, W. Li, Y. Wang, L. Zhu, M. Zhao, Y. Yu, Trans. Tianjin Univ., 2023, 29, 275-283.
[19]	C. Sellers, T. P. Senftle, Nat. Energy, 2023, 8, 1184-1185.
[20]	Y. Lin, J. Liang, H. Li, L. Zhang, T. Mou, T. Li, L. Yue, Y. Ji, Q. Liu, Y. Luo, N. Li, B. Tang, Q. Wu, M. S. Hamdy, D. Ma, X. Sun, Mater. Today Phys., 2022, 22, 100611.
[21]	K. Chen, G. Zhang, X. Li, X. Zhao, K. Chu, Nano Res., 2023, 16, 5857-5863.
[22]	R. T. Hannagan, G. Giannakakis, M. Flytzani-Stephanopoulos, E. C. H. Sykes, Chem. Rev., 2020, 120, 12044-12088.
[23]	J. Mao, J. Yin, J. Pei, D. Wang, Y. Li, Nano Today, 2020, 34, 100917.
[24]	Y. Xing, L. Kang, J. Ma, Q. Jiang, Y. Su, S. Zhang, X. Xu, L. Li, A. Wang, Z.-P. Liu, S. Ma, X. Y. Liu, T. Zhang, Chin. J. Catal., 2023, 48, 164-174.
[25]	S. Yang, Z. Si, G. Li, P. Zhan, C. Liu, L. Lu, B. Han, H. Xie, P. Qin, Small, 2023, 19, 2207651.
[26]	J.-C. Jiang, J.-C. Chen, M.-D. Zhao, Q. Yu, Y.-G. Wang, J. Li, Nano Res., 2022, 15, 7116-7123.
[27]	D. Wang, R. Cao, S. Hao, C. Liang, G. Chen, P. Chen, Y. Li, X. Zou, Green Energy Environ., 2023, 8, 820-830.
[28]	S. Baskaran, C.-Q. Xu, Y.-G. Wang, I. L. Garzón, J. Li, Sci. China Mater., 2020, 63, 993-1002.
[29]	X. Cheng, Y. Wang, Y. Lu, L. Zheng, S. Sun, H. Li, G. Chen, J. Zhang, Appl. Catal. B, 2022, 306, 121112.
[30]	S. Wang, X.-T. Min, B. Qiao, N. Yan, T. Zhang, Chin. J. Catal., 2023, 52, 1-13.
[31]	T. Sheng, N. Tian, Z.-Y. Zhou, W.-F. Lin, S.-G. Sun, ACS Energy Lett., 2017, 2, 1892-1900.
[32]	M. D. Marcinkowski, M. T. Darby, J. Liu, J. M. Wimble, F. R. Lucci, S. Lee, A. Michaelides, M. Flytzani-Stephanopoulos, M. Stamatakis, E. C. H. Sykes, Nat. Chem., 2018, 10, 325-332.
[33]	Z. Li, T. He, D. Matsumura, S. Miao, A. Wu, L. Liu, G. Wu, P. Chen, ACS Catal., 2017, 7, 6762-6769.
[34]	J. K. Nørskov, J. Rossmeisl, A. Logadottir, L. Lindqvist, J. R. Kitchin, T. Bligaard, H. Jónsson, J. Phys. Chem. B, 2004, 108, 17886-17892.
[35]	I. C. Man, H.-Y. Su, F. Calle-Vallejo, H. A. Hansen, J. I. Martínez, N. G. Inoglu, J. Kitchin, T. F. Jaramillo, J. K. Nørskov, J. Rossmeisl, ChemCatChem, 2011, 3, 1159-1165.
[36]	J. Long, S. Chen, Y. Zhang, C. Guo, X. Fu, D. Deng, J. Xiao, Angew. Chem. Int. Ed., 2020, 59, 9711-9718.
[37]	F. Calle-Vallejo, J. I. Martínez, J. M. García-Lastra, J. Rossmeisl, M. T. M. Koper, Phys. Rev. Lett., 2012, 108, 116103.
[38]	Z. W. Seh, J. Kibsgaard, C. F. Dickens, I. Chorkendorff, J. K. Nørskov, T. F. Jaramillo, Science, 2017, 355, eaad4998.
[39]	X.-R. Wang, J.-Y. Liu, Z.-W. Liu, W.-C. Wang, J. Luo, X.-P. Han, X.-W. Du, S.-Z. Qiao, J. Yang, Adv. Mater., 2018, 30, 1800005.
[40]	J. Zhang, J. Liu, L. Xi, Y. Yu, N. Chen, S. Sun, W. Wang, K. M. Lange, B. Zhang, J. Am. Chem. Soc., 2018, 140, 3876-3879.
[41]	Z.-W. Gao, J.-Y. Liu, X.-M. Chen, X.-L. Zheng, J. Mao, H. Liu, T. Ma, L. Li, W.-C. Wang, X.-W. Du, Adv. Mater., 2019, 31, 1804769.
[42]	A. Vojvodic, J.K. Nørskov, Nat. Sci. Rev., 2015, 2, 140-143.
[43]	G. Kresse, J. Furthmüller, Comput. Mater. Sci., 1996, 6, 15-50.
[44]	G. Kresse, D. Joubert, Phys. Rev. B, 1999, 59, 1758-1775.
[45]	J. P. Perdew, K. Burke, M. Ernzerhof, Phys. Rev. Lett., 1996, 77, 3865-3868.
[46]	J. P. Perdew, J. A. Chevary, S. H. Vosko, K. A. Jackson, M. R. Pederson, D. J. Singh, C. Fiolhais, Phys. Rev. B, 1992, 46, 6671-6687.
[47]	J. P. Perdew, Y. Wang, Phys. Rev. B, 1992, 45, 13244-13249.
[48]	P. E. Blöchl, Phys. Rev. B, 1994, 50, 17953-17979.
[49]	V. I. Anisimov, J. Zaanen, O. K. Andersen, Phys. Rev. B, 1991, 44, 943-954.
[50]	H. Xu, D. Cheng, D. Cao, X.C. Zeng, Nat. Catal., 2024, 7, 207-218.
[51]	S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys., 2010, 132, 154104.
[52]	K. Mathew, R. Sundararaman, K. Letchworth-Weaver, T. A. Arias, R. G. Hennig, J. Chem. Phys., 2014, 140, 084106.
[53]	J. K. Nørskov, T. Bligaard, A. Logadottir, J. R. Kitchin, J. G. Chen, S. Pandelov, U. Stimming, J. Electrochem. Soc., 2005, 152, J23-J26.
[54]	G. Di Liberto, L. Giordano, G. Pacchioni, ACS Catal., 2024, 14, 45-55.
[55]	T. Bligaard, J. K. Nørskov, S. Dahl, J. Matthiesen, C. H. Christensen, J. Sehested, J. Catal., 2004, 224, 206-217.
[56]	K. Mersmann, K. H. Horn, N. Böres, N. Lehnert, F. Studt, F. Paulat, G. Peters, I. Ivanovic-Burmazovic, R. van Eldik, F. Tuczek, Inorg. Chem., 2005, 44, 3031-3045.
[57]	X. Guo, J. Gu, S. Lin, S. Zhang, Z. Chen, S. Huang, J. Am. Chem. Soc., 2020, 142, 5709-5721.
[58]	E. Skúlason, T. Bligaard, S. Gudmundsdóttir, F. Studt, J. Rossmeisl, F. Abild-Pedersen, T. Vegge, H. Jónsson, J. K. Nørskov, Phys. Chem. Chem. Phys., 2012, 14, 1235-1245.
[59]	X. Zhang, Y. Tian, L. Chen, X. Hu, Z. Zhou, J. Phys. Chem. Lett., 2022, 13, 7920-7930.
[60]	L. Chen, X. Zhang, A. Chen, S. Yao, X. Hu, Z. Zhou, Chin. J. Catal., 2022, 43, 11-32.
[61]	F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, É. Duchesnay, J. Machine Learning Res., 2011, 12, 2825-2830.
[62]	M. O. J. Jäger, E. V. Morooka, F. Federici Canova, L. Himanen, A. S. Foster, NPJ Comput. Mater., 2018, 4, 37.
[63]	J. Zheng, X. Sun, C. Qiu, Y. Yan, Z. Yao, S. Deng, X. Zhong, G. Zhuang, Z. Wei, J. Wang, J. Phys. Chem. C, 2020, 124, 13695-13705.
[64]	X. Guo, S. Lin, J. Gu, S. Zhang, Z. Chen, S. Huang, ACS Catal., 2019, 9, 11042-11054.
[65]	G. Zheng, Y. Li, X. Qian, G. Yao, Z. Tian, X. Zhang, L. Chen, ACS Appl. Mater. Interfaces, 2021, 13, 16336-16344.
[66]	S. Lin, H. Xu, Y. Wang, X. C. Zeng, Z. Chen, J. Mater. Chem. A, 2020, 8, 5663-5670.
[67]	R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, L. M. Ghiringhelli, Phys. Rev. Mater., 2018, 2, 083802.
[68]	F. Chen, J. Wang, Z. Guo, F. Jiang, R. Ouyang, P. Ding, ACS Appl. Mater. Interfaces, 2021, 13, 53425-53438.
[69]	S. Zhu, M. Qin, L. Chen, S. Jiang, Y. Zhou, J. Jiang, W. Zhang, J. Phys. Chem. Lett., 2023, 14, 4185-4191.

基于DFT和机器学习的Pt单原子合金电催化剂的理性设计: 应用于NO至NH₃的转化

Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH₃ conversion by density functional theory and machine learning

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