利用机器学习靶向设计先进电催化剂

doi:10.1016/S1872-2067(21)63852-4

催化学报 ›› 2022, Vol. 43 ›› Issue (1): 11-32.DOI: 10.1016/S1872-2067(21)63852-4

利用机器学习靶向设计先进电催化剂

陈乐添^a, 张旭^a^,^b^,^#(), 陈安^a, 姚赛^a, 胡绪^a, 周震^a^,^b^,^*()

^a南开大学材料科学与工程学院, 新能源材料化学研究所, 新能源转化与存储交叉科学中心, 先进能源材料化学教育部重点实验室, 天津300350
^b郑州大学化工学院, 先进功能材料制造教育部工程研究中心, 河南郑州450001

收稿日期:2021-05-11 接受日期:2021-05-28 出版日期:2022-01-18 发布日期:2021-07-07
通讯作者: 张旭,周震
基金资助:
国家自然科学基金(91845112);中国博士后科学基金(2019M660055)

Targeted design of advanced electrocatalysts by machine learning

Letian Chen^a, Xu Zhang^a^,^b^,^#(), An Chen^a, Sai Yao^a, Xu Hu^a, Zhen Zhou^a^,^b^,^*()

^aSchool of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education), Nankai University, Tianjin 300350, China
^bEngineering Research Center of Advanced Functional Material, Manufacturing of Ministry of Education, School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China

Received:2021-05-11 Accepted:2021-05-28 Online:2022-01-18 Published:2021-07-07
Contact: Xu Zhang,Zhen Zhou
About author:^# E-mail: zhangxu@nankai.edu.cn
* E-mail: zhenzhou@zzu.edu.cn;
Supported by:
National Natural Science Foundation of China(91845112);China Postdoctoral Science Foundation(2019M660055)

摘要/Abstract

摘要：

随着能源需求增长与化石燃料资源枯竭之间的矛盾日益突出, 以及石油、天然气等不可再生资源的燃烧带来的环境问题和全球变暖, 清洁可再生能源越来越受到人们的重视. 因此, 包括能源转换和可逆能源使用等的可持续发展技术受到广泛关注. 其中, 电催化被认为是清洁能源转化的重要方法. 目前, 电催化反应的催化剂仍以贵金属为主. 但贵金属昂贵的价格极大地限制了其使用, 因此, 开发廉价高效的电催化剂取代贵金属成为当务之急. 传统的“试错法”费时费力且成本较高. 近年来, 随着超级计算机和计算理论的快速发展, 密度泛函理论(DFT)和高通量计算可以指导材料的设计. 尽管如此, 要从巨大的化学空间中筛选出先进的电催化剂, 使清洁能源技术得以广泛普及, 仍是一个难题. 幸运的是, 跨学科融合及机器学习算法的发展为电催化剂的靶向设计注入新的动力. 机器学习已经能够以接近DFT的计算精度模拟电化学过程.
本文概述了机器学习方法在指导先进电催化剂的靶向设计中的应用, 包括结构、热力学和动力学性质的推测、简单真空环境下的近似能量预测和考虑显式溶剂分子的复杂带电界面的模拟. 除了基于机器学习的直接预测外, 还介绍了通过拟合势能面或构造力场的方法来模拟催化反应中的动力学过程. 在已有研究的基础上, 展望了机器学习在固液界面模拟中的应用前景. 本文提出的集成的机器学习模型有望有效地应用于恒电位下电催化界面的模拟, 同时, 用机器学习方法研究电化学过程将进一步推动高效电催化剂的靶向设计.

关键词: 电催化剂, 机器学习, 靶向设计, 热力学性质, 动力学势垒

Abstract:

Exploring the production and application of clean energy has always been the core of sustainable development. As a clean and sustainable technology, electrocatalysis has been receiving widespread attention. It is crucial to achieve efficient, stable and cheap electrocatalysts. However, the traditional “trial and error” method is time-consuming, laborious and costly. In recent years, with the significant increase in computing power, computations have played an important role in electrocatalyst design. Nevertheless, it is still difficult to search for advanced electrocatalysts in the vast chemical space through traditional density functional theory (DFT) computations. Fortunately, the development of machine learning and interdisciplinary integration will inject new impetus into targeted design of electrocatalysts. Machine learning is able to predict electrochemical performances with an accuracy close to DFT. Here we provide an overview of the application of machine learning in electrocatalyst design, including the prediction of structure, thermodynamic properties and kinetic barriers. We also discuss the potential of explicit solvent model combined with machine learning molecular dynamics in this field. Finally, the favorable circumstances and challenges are outlined for the future development of machine learning in electrocatalysis. The studies on electrochemical processes by machine learning will further realize targeted design of high-efficiency electrocatalysts.

Key words: Electrocatalyst, Machine learning, Targeted design, Thermodynamics properties, Kinetic barrier

陈乐添, 张旭, 陈安, 姚赛, 胡绪, 周震. 利用机器学习靶向设计先进电催化剂[J]. 催化学报, 2022, 43(1): 11-32.

Letian Chen, Xu Zhang, An Chen, Sai Yao, Xu Hu, Zhen Zhou. Targeted design of advanced electrocatalysts by machine learning[J]. Chinese Journal of Catalysis, 2022, 43(1): 11-32.

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图/表 14

Fig. 1. Schematic illustration for the role of machine learning in electrocatalysis.

Fig. 2. (a) Schematic illustration of the procedure for prediction of new compounds by using DMSP. Adapted with permission from Ref. [17]. Copyright 2011, American Chemical Society. (b) Discovery histogram for new ternary nitrides based on entries catalogued in ICSD. Adapted with permission from Ref. [51]. Copyright 2019, Nature.

Fig. 3. (a) Breeding processes in GAs for two example parents Ni6@Pt32 and Co6@Pt32. Adapted with permission from Ref. [19]. Copyright 2009, AIP publishers. (b) A schematic plot of generative machine learning model based on a generative adversarial network for inorganic materials. Adapted with permission from Ref. [22]. Copyright 2020, the Authors.

Fig. 4. (a) The confusion matrix with the F-Measure values of classification models. Adapted with permission from Ref. [49]. Copyright 2020, the Authors. (b) The convex hull located by ML-based GA. Adapted with permission from Ref. [23]. Copyright 2019, the Authors.

Fig. 5. (a) Relationship between the descriptor found by LASSO and overpotential of OER. Adapted with permission from Ref. [72]. Copyright 2020, American Chemical Society. (b) Relationship between the descriptor found by LASSO and Gibbs free energy of first hydrogenation for NRR. Inset: the optimized structures for N2 on 2D Ti@VB2. Adapted with permission from Ref. [73]. Copyright 2020, American Chemical Society. CO adsorption energies calculated by DFT vs. the one predicted by (c) the two-level interaction model or (d) ML model. Insets: sketches of (c) the two-level interaction model and (d) the artificial neural network models. (c,d) Adapted with permission from Ref. [74]. Copyright 2015, American Chemical Society. Comparison of the performance of ML for predicting adsorption energies trained with (e) the geometry based and (f) electronic structured based primary features. (e,f) Adapted with permission from Ref. [75]. Copyright 2017, Elsevier.

Fig. 6. (a) The limiting potential steps for MOR plotted with CO and OH adsorption free energies relative to Pt(111). Adapted with permission from Ref. [81]. Copyright 2017, Royal Society of Chemistry. (b) Performance of the GBR model for predicting adsorption energies of different adsorbates: CO (left), CHx (middle), and CO/CHO/COH (right). Insets: the feature importance scores of different descriptors (upper left corner) and the prediction error distribution (lower right corner). (c) Performance of the GBR model for predicting adsorption energies of adsorbates (11 species). Insets: the feature importance scores of different descriptors and the prediction error distribution. (b,c) Adapted with permission from Ref. [83]. Copyright 2017, Royal Society of Chemistry.

Fig. 7. (a) Heat map of the prediction values for CO adsorption energies. Adapted with permission from Ref. [85]. Copyright 2020, American Chemical Society. (b) t-SNE visualization of approximately 4000 adsorption sites simulated with DFT. (c) The sites for each cluster labelled in (b). (d) 2D selectivity volcano plot for CO2RR [91]. Copyright 2020, Nature. (e) The adsorption Gibbs free energies of N2 versus the adsorption Gibbs free energies of H for 23 transition metals. (b-e) Adapted with permission from Ref. [26]. Copyright 2020, American Chemical Society. (f) Visualization of catalytic sites on dealloyed gold surface. Adapted with permission from Ref. [92]. Copyright 2019, American Chemical Society.

Fig. 8. (a) Distribution of OH adsorption energies for 871 cells. Each individual on-top binding site is represented by a color. Adapted with permission from Ref. [25]. Copyright 2019, Elsevier. (b) iR-corrected LSV curve of Al0.5Mn2.5O4. Inset: the volcano plot where the location of Al0.5Mn2.5O4 is. The volcano plot represents the relationship between the experimentally observed reaction activity and the calculated Max(DT, DO), where DT is the distance between O p-band center and tetrahedral metal d-band center, and DO represents the distance from O p-band center to octahedral metal d-band center. Adapted with permission from Ref. [104]. Copyright 2019, Nature.

Fig. 9. Schematic diagram of (a) the FFNN for fitting PES and (b) the HDNN framework for a ternary system with A, B and C elements. (a,b) Adapted with permission from Ref. [38]. Copyright 2017, Wiley-VCH. (c) The process for the fitting of the PES by HDNN. Adapted with permission from Ref. [121]. Copyright 2015, American Chemical Society. (d) H-coupling reaction path of H2 production on amorphous surface of TiO2-0.69H. Adapted with permission from Ref. [39]. Copyright 2018, American Chemical Society. (e) Phase diagram of ternary Zn-Cr-O. Adapted with permission from Ref. [122]. Copyright 2019, Nature. (f) Reaction path of the acetylene hydrogenation on Pd(111) and Pd4H3(111). Adapted with permission from Ref. [123]. Copyright 2020, American Chemical Society.

Fig. 10. (a) Reaction network for the reaction of syngas to CO2, H2O, CH3OH, CH3CHO, CH4, CH3CH2OH and other intermediates. (b) The structure of Rh(111) and the reduced reaction network for syngas reaction on Rh(111) surface. The cyan, red, grey and white spheres represent Rh, O, C, and H atoms, respectively. Adapted with permission from Ref. [36]. Copyright 2017, the Authors.

Fig. 11. (a) Schematic of the BR used to predict energetics of reaction intermediates on nanoparticles. The training data of adsorbate binding energies on single-crystal surfaces and small clusters were calculated by DFT. Ek, Kkj, and wj represent the energy of kth reaction intermediate on nanoparticles, the SOAP kernel, and the regression coefficients, respectively [37]. Copyright 2017, American Chemical Society. (b) Predicted TOFs of each surface site at 500 K. Inset: a structure of the active site on Rh(1-x)Aux. Adapted with permission from Ref. [130]. Copyright 2017, American Chemical Society.

Fig. 13. (a) The electronic energy E and corresponding work function Φ as a function of reaction coordinates. (b) Reaction and activation energies calculated at different cell sizes ai vs. the change in potential and extrapolation of reaction and activation energies from unit cell size a0 to infinite cell size a∞. (c) Parity plot of reaction energies and barriers obtained by cell-extrapolation and the charge-extrapolation. Adapted with permission from Ref [158]. Copyright 2015, American Chemical Society.

Fig. 14. The prospect of machine learning in the explicit solvent model under constant potentials. The right part is the atomic structure of an electrocatalyst in an aqueous solution, including a porous carbon electrode, electrified interface and electrolyte solution. Adapted with permission from Ref [162]. Copyright 2021, Nature.

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利用机器学习靶向设计先进电催化剂

Targeted design of advanced electrocatalysts by machine learning

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