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A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts
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C: Surfaces, Interfaces, Porous Materials, and Catalysis

A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts
基于 CO2 还原电催化剂简单特征的机器学习模型
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  • An Chen
    An Chen
    School 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, P. R. China
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  • Xu Zhang*
    Xu Zhang
    School 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, P. R. China
    *Email: zhangxu@nankai.edu.cn
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  • Letian Chen
    Letian Chen
    School 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, P. R. China
    More by Letian Chen
  • Sai Yao
    Sai Yao
    School 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, P. R. China
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  • Zhen Zhou*
    Zhen Zhou
    School 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, P. R. China
    *Email: zhouzhen@nankai.edu.cn
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The Journal of Physical Chemistry C

Cite this: J. Phys. Chem. C 2020, 124, 41, 22471–22478
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https://doi.org/10.1021/acs.jpcc.0c05964
Published September 16, 2020
Copyright © 2020 American Chemical Society

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Abstract  抽象

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Electroreduction of CO2 is one of the most potential ways to realize CO2 recycle and energy regeneration. The key to promoting this technology is the development of high-performance electrocatalysts. Generally, high-throughput computational screening contributes a lot to materials innovation, but still consumes much time and resource. To achieve efficient exploration of electrocatalysts for CO2 reduction, we created a machine learning model based on an extreme gradient boosting regression (XGBR) algorithm and simple features. Our screening model successfully and rapidly predicted the Gibbs free energy change of CO adsorption (ΔGCO) of 1060 atomically dispersed metal–nonmetal codoped graphene systems, and greatly reduced the research cost. The competitive reaction, the hydrogen evolution reaction (HER), is also discussed with respect to such a screening model. This work demonstrates the potential of machine learning methods and provides a convenient approach for the effective theoretical design of electrocatalysts for CO2 reduction.
电还原 CO2 是实现 CO2 回收和能源再生的最有潜力的方法之一。推广这项技术的关键是开发高性能电催化剂。一般来说,高通量计算筛选对材料创新有很大贡献,但仍然会消耗大量的时间和资源。为了实现对用于 CO2 还原的电催化剂的有效探索,我们创建了一个基于极端梯度提升回归 (XGBR) 算法和简单特征的机器学习模型。我们的筛选模型成功快速地预测了 1060 个原子分散的金属-非金属共掺杂石墨烯体系的 CO 吸附 (ΔGCO) 的吉布斯自由能变化,大大降低了研究成本。还讨论了竞争反应,即析氢反应 (HER),与这种筛选模型有关。这项工作展示了机器学习方法的潜力,并为还原 CO2 的电催化剂的有效理论设计提供了一种便捷的方法。

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Copyright © 2020 American Chemical Society
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Special Issue  所属专题

Published as part of The Journal of Physical Chemistry virtual special issue “Machine Learning in Physical Chemistry”.
作为 The Journal of Physical Chemistry 虚拟特刊“Machine Learning in Physical Chemistry”的一部分发布。

Introduction  介绍

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Traditional energy conversion technologies are unable to meet the current demand. Thus, sustainable development and reuse of energy are of great significance nowadays. To achieve larger-scale energy reuse, new strategies are urgently needed. Electroreduction of carbon dioxide is an important process of sustainable production of fuels and value-added chemicals. (1) For this process, electrocatalysts are decisive. Bulk metals and alloys, (2,3) nonmetals, (4,5) and nanostructured materials (6,7) have been applied to this domain. Moreover, experiments and computations have demonstrated successful applications of carbon-based materials toward electroreduction of CO2. (8−10) A kind of modified carbon-based catalysts with isolated single-atom sites, generally called single-atom catalysts (SACs), (11) shows the potential for the electroreduction process due to its distinctive structures and properties. Pan et al. (12) designed such catalysts for CO2 electroreduction, with N-doped porous carbon spheres as the substrate to disperse Co atoms. These unique catalysts show both excellent selectivity and high stability for CO2 electroreduction. Liu et al. (13) introduced Fe-doped graphdiyne (Fe/GDY) to CO2 eletroreduction by means of density functional theory (DFT) computations. The results reveal the application potential of Fe/GDY, and the stability is also guaranteed by strong hybridization between Fe and GDY. Taking this kind of unique and controllable structures as the substrate brings many new ideas to materials design, which extends the content of novel catalysts.
传统的能源转换技术无法满足当前的需求。因此,能源的可持续发展和再利用在当今具有重要意义。为了实现更大规模的能源再利用,迫切需要新的策略。二氧化碳的电解还原是可持续生产燃料和增值化学品的重要过程。(1) 对于此过程,电催化剂是决定性的。块状金属和合金、(2,3) 非金属、(4,5) 和纳米结构材料 (6,7) 已应用于该领域。此外,实验和计算已经证明了碳基材料在电还原 CO2 方面的成功应用。(8−10) 一种具有孤立的单原子位点的改性碳基催化剂,通常称为单原子催化剂 (SAC),(11) 由于其独特的结构和性质,显示出电还原过程的潜力。Pan 等人 (12) 设计了这种用于 CO2 电还原的催化剂,以 N 掺杂多孔碳球作为分散 Co 原子的基材。这些独特的催化剂对 CO2 电还原表现出优异的选择性和高稳定性。Liu 等人 (13) 通过密度泛函理论 (DFT) 计算将 Fe 掺杂石墨炔 (Fe/GDY) 引入 CO2 电还原。结果揭示了 Fe/GDY 的应用潜力,Fe 和 GDY 的强杂化也保证了稳定性。以这种独特可控的结构为基材,为材料设计带来了许多新的思路,扩展了新型催化剂的内涵。
High-throughput experiments and computations have contributed much to the accurate preparation of new materials with sophisticated structures for CO2 electroreduction. However, the cost of time and money for high-throughput investigations is huge, and the development of novel SACs is constricted. At this point, it is very urgent to optimize the efficient screening methods for SACs.
高通量实验和计算为准确制备具有复杂结构的 CO2 电还原新材料做出了重大贡献。然而,高通量研究的时间和金钱成本是巨大的,并且新型 SAC 的开发受到限制。在这一点上,优化 SACs 的高效筛选方法非常紧迫。
Machine learning (ML) has been applied to explore complicated materials for energy conversion, (14) such as MXenes, (15) perovskites, (16−18) and metal–organic frameworks (MOFs). (19−21) For SACs, machine learning has also been used as a powerful auxiliary tool. Zhu and co-workers (22) applied machine learning models based on DFT computations to explore the design principles of dual-metal-site catalysts (DMSCs) for the oxygen reduction reaction (ORR). By the best prediction model, several novel DMSCs were filtered with outstanding ORR activity compared with that of platinum. By using a deep neural network (DNN) algorithm, Zafari et al. (23) predicted three novel SACs for the nitrogen reduction reaction (NRR) from the results gained from a machine learning model. However, machine learning investigations on single-atom electrocatalysts for CO2 reduction are lacking, and this technology is definitely helpful for this purpose. To be more comprehensive, ML-assisted materials design always selects features from both geometrical and electronic structures. In fact, the databases of complicated materials, especially for novel materials not prepared experimentally yet, are generally established by computations. If too many features are considered, the computational cost will be multiplied.
机器学习 (ML) 已被应用于探索用于能量转换的复杂材料,(14), 例如 MXenes、(15) 钙钛矿、(16-18) 和金属有机框架 (MOF)。(19−21) 对于 SAC,机器学习也被用作强大的辅助工具。Zhu 及其同事 (22) 应用基于 DFT 计算的机器学习模型来探索用于氧还原反应 (ORR) 的双金属位催化剂 (DMSC) 的设计原理。通过最佳预测模型,过滤了几种新型 DMSC,与铂相比,具有出色的 ORR 活性。通过使用深度神经网络 (DNN) 算法,Zafari 等人 (23) 根据机器学习模型获得的结果预测了氮还原反应 (NRR) 的三种新型 SAC。然而,缺乏对用于 CO2 还原的单原子电催化剂的机器学习研究,这项技术绝对有助于实现这一目的。更全面地说,ML 辅助材料设计始终从几何和电子结构中选择特征。事实上,复杂材料的数据库,特别是尚未通过实验制备的新型材料的数据库,通常是通过计算建立的。如果考虑的特征过多,计算成本将成倍增加。
In this work, we use machine learning with simple and easily accessible features to train prediction models that can accurately describe the catalytic performance of single-atom catalysts with dispersed metal–nonmetal codoped graphene structures (for convenient expression, this kind of materials will be presented by SACs) (Figure 1). Combining with the excellent properties of this kind of materials, our work for mining novel SACs can accelerate the development of CO2 reduction electrocatalysts. The first part is building a database for the target electrocatalysts by means of DFT computations. Then we focus on the establishment of machine learning models. Finally, we predict potential catalysts for CO2 electroreduction through predicted ΔGCO, which was widely used to explore CO2 electroreduction mechanisms and catalytic performance in previous studies. (24−26) Our work provides guidance to explore CO2 reduction electrocatalysts, and the selection of simple features is also helpful for other fast DFT-machine learning combining methods.
在这项工作中,我们使用具有简单易懂功能的机器学习来训练预测模型,这些模型可以准确描述具有分散金属-非金属共掺杂石墨烯结构的单原子催化剂的催化性能(为方便表达,这类材料将由 SAC 呈现)(1)。结合这种材料的优异性能,我们开采新型 SAC 的工作可以加速 CO2 还原电催化剂的开发。第一部分是通过 DFT 计算为目标电催化剂构建数据库。然后我们专注于建立机器学习模型。最后,我们通过预测的 ΔGCO 预测 CO2 电还原的潜在催化剂,这在以前的研究中被广泛用于探索 CO2 电还原机制和催化性能。(24−26) 我们的工作为探索 CO2 还原电催化剂提供了指导,简单特征的选择也有助于其他快速 DFT 机器学习组合方法。

Figure 1  图 1

Figure 1. Structure of SAC. Shallow blue, green, and gray balls represent transition metal (Ag, Au, Co, Cu, Fe, Ir, Mn, Mo, Ni, Os, Pd, Pt, Rh, Ru, Sc, Ti, V, Y, Zn or Zr), nonmetal (C, N, O, P, or S), and C atoms, respectively.
图 1.SAC 的结构。浅蓝色、绿色和灰色球分别代表过渡金属(Ag、Au、Co、Cu、Fe、Ir、Mn、Mo、Ni、Os、Pd、Pt、Rh、Ru、Sc、Ti、V、Y、Zn 或 Zr)、非金属(C、N、O、P 或 S)和 C 原子。

Computational Details  计算详细信息

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Establishment of a Database
建立数据库

All the data were obtained from DFT computations that were performed with a plane-wave basis set as implemented in the Vienna ab initio simulation package (VASP). (27) The magnetic ions were initialized ferromagnetically. The projector augmented wave (PAW) was adopted to describe the ion-electron interactions. (28) Revised Perdew–Burke–Ernzerhof (RPBE) (29) was used to describe the adsorption. An energy cutoff of 450 eV was employed for the plane-wave basis set. A Monkhorst–Pack k point mesh of 4 × 5 × 1 was used to sample the first Brillouin zone. (30) Considering the strong electron correlation of some transition metal elements, the DFT+U with U-J parameters was used, and the corresponding U-J parameters were used in accordance with a previous report. (31) To prevent any artificial interactions between two neighboring structures, a vacuum space with 20 Å was inserted in the corresponding direction. To evaluate the stability of the catalysts, ab initio molecular dynamics (AIMD) simulations were performed in a NVT ensemble and lasted for 10 ps with a time step of 2 fs at 600 K. The temperature was controlled by the Nosé–Hoover method. (32)
所有数据均来自 DFT 计算,这些计算是使用 Vienna ab initio 仿真包 (VASP) 中实现的平面波基集执行的。(27) 磁离子以铁磁方式初始化。采用投影仪增强波 (PAW) 来描述离子-电子相互作用。(28) 修订的 Perdew-Burke-Ernzerhof (RPBE) (29) 用于描述吸附。平面波基集采用 450 eV 的能量截止。使用 4 × 5 × 1 的 Monkhorst-Pack k 点网格对第一个布里渊区进行采样。(30) 考虑到一些过渡金属元素的强电子相关性,使用了具有 U-J 参数的 DFT+U,并根据以前的报告使用了相应的 U-J 参数。(31) 为了防止两个相邻结构之间发生任何人为的相互作用,在相应的方向上插入了一个 20 Å 的真空空间。为了评估催化剂的稳定性,在 NVT 集合中进行了从头计算分子动力学 (AIMD) 模拟,并在 600 K 下以 2 fs 的时间步长持续 10 ps。温度由 Nosé-Hoover 方法控制。(32)
The free energy change during adsorption of an adsorbate X can be calculated by (33)
吸附物 X 吸附过程中的自由能变化可由下式计算 (33)
where μX* is the chemical potential of the adsorbate-catalyst complex, μX is the chemical potential of the adsorbate in the gas phase, and μ* is the chemical potential of the catalyst. The chemical potential can be computed according to (33)
其中 μX* 是吸附物-催化剂复合物的化学势,μX 是吸附物在气相中的化学势,μ* 是催化剂的化学势。化学势可以根据 (33) 计算
in which E is the energy calculated by DFT, EZPE is zero point energy which was computed based on the vibrational frequencies, Cp is the heat capacity, T represents temperature, S is entropy which could be obtained from NIST (National Institute of Standards and Technology) database for gas phase, Δμsolv stands for the change in chemical potential from solvent stabilization, and Δμcorr means the experimental correction to account for the difference between experimental chemical potentials and DFT-based chemical potentials.
其中 E 是由 DFT 计算的能量,EZPE 是根据振动频率计算的零点能量,CP 是热容,T 代表温度,S 是熵,可以从 NIST(美国国家标准与技术研究所)气相数据库获得,Δμsolv 代表溶剂稳定引起的化学势变化, Δμcorr 表示解释实验化学势和基于 DFT 的化学势之间差异的实验校正。
According to previous reports, (33,34)Cp,CO dT is 0.091 eV at 298 K and Δμcorr,CO is 0.02 eV for CO gas. For adsorbed CO, ∫Cp,CO* dT is 0.085 eV. For adsorbates, only vibrational entropy contributions were considered, which is consistent with a previous report. (35) The values of Δμsolv,CO* and Δμcorr,CO* are in agreement with those in a previous report. (33)
根据以前的报告,(33,34)Cp,CO dT 在 298 K 时为 0.091 eV,Δμcorr,CO 为 0.02 eV。 对于吸附的 CO,∫Cp,CO* dT 为 0.085 eV。对于吸附物,只考虑了振动熵贡献,这与之前的报告一致。(35) Δμsolv,CO* 和 Δμcorr,CO* 的值与之前报告中的值一致。(33)
SAC structures of the database are presented in Figure S1. Not only new SACs are included in this data set, but also some reported structures, such as Mn–N2C2, (36) Ni–N2O2, Ni–N4, (37) and Co–N4. (38)
数据库的 SAC 结构如图 S1 所示。该数据集中不仅包括新的 SAC,还包括一些已报道的结构,例如 Mn-N2C2、(36) Ni-N2O2、Ni-N4、(37) 和 Co-N4。(38)

Machine Learning Methods  机器学习方法

In this work, we tried more than one ML algorithm, such as K-nearest neighbor regression (KNR), random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), extreme gradient boosting regression (39) (XGBR), and a kind of composited algorithms produced by TPOT (tree-based pipeline optimization tool). (40) For each algorithm, we tested various pairs of training and testing data with different separated ratios to gain ideal regression models.
在这项工作中,我们尝试了不止一种 ML 算法,例如 K 最近邻回归 (KNR)、随机森林回归 (RFR)、支持向量回归 (SVR)、梯度提升回归 (GBR)、极端梯度提升回归 (39) (XGBR),以及 TPOT (tree-based pipeline optimization tool) 产生的一种复合算法。(40) 对于每种算法,我们测试了具有不同分离比率的各种训练和测试数据对,以获得理想的回归模型。
The performance of feature sets was evaluated by the Pearson correlation coefficient (p), which is determined by
特征集的性能由 Pearson 相关系数 (p) 评估,该系数由
in which fi and Fi are the compared features. The value of p is in the range of −1.0 to 1.0, and a higher absolute value of p presents a stronger correlation.
其中 fF 是比较的特征。p 的值在 −1.0 到 1.0 的范围内,p 的绝对值越高,相关性越强。
The coefficient of determination (r (2)) and root mean squared error (RMSE) were used to reflect the prediction accuracy of the models, and are defined as
决定系数 (r(2)) 和均方根误差 (RMSE) 用于反映模型的预测准确性,定义为
where Yi indicates the value of the DFT computations, yi indicates the result predicted by the ML models, and indicates the average value of the DFT data. Generally, an ideal model should have r2 value close to 1, and a small RMSE close to 0.
其中 Y 表示 DFT 计算的值,y 表示 ML 模型预测的结果, 表示 DFT 数据的平均值。通常,理想模型的 r2 值应接近 1,小 RMSE 接近 0。
We implemented the computations with the Scikit-Learn package. (41) Important parameters of machine learning models are given in Table S1, Supporting Information.
我们使用 Scikit-Learn 包实现了计算。(41) 机器学习模型的重要参数在表 S1 支持信息中给出。

Results and Discussion  结果与讨论

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Determination of Target Features for CO2 Electroreduction
确定 CO2 电还原的目标特性

Before the establishment of prediction models, the evaluation parameter for the CO2 reduction reaction (CO2RR) and target features for machine learning models should be determined. Thermodynamic energies of microkinetic models or experimental characterizations have been used to represent catalytic performance. Moreover, it is a common and mature approach to use the adsorption energies or the Gibbs free energy change of intermediates to assess and predict electrocatalytic performances, such as hydrogen evolution reaction (HER) and NRR. In this work, toward CO2RR, we chose the widely accepted computational hydrogen electrode model to present catalytic reactions and develop free energy profiles for the reactions. (42) As is known, CO is an important intermediate of multicarbon hydrocarbons and oxygenates in CO2RR, (43) and previous relevant studies demonstrated that the CO adsorption energy is an ideal value to describe catalytic performance for CO2RR. (44,45) Although choosing a single value as the evaluation criterion and target feature would undoubtedly lead to defective results, the original intention of our work is to preliminarily screen new catalysts efficiently with low research cost. Thus, it is reasonable to consider only vital features for the balance between accuracy and consumption.
在建立预测模型之前,应确定 CO2 还原反应 (CO2RR) 的评估参数和机器学习模型的目标特征。微动力学模型或实验表征的热力学能量已被用于表示催化性能。此外,利用中间体的吸附能或吉布斯自由能变化来评估和预测析氢反应 (HER) 和 NRR 等电催化性能是一种常见且成熟的方法。在这项工作中,针对 CO2RR,我们选择了被广泛接受的计算氢电极模型来呈现催化反应并为反应开发自由能曲线。(42) 众所周知,CO 是 CO2RR 中多碳烃和含氧化合物的重要中间体,(43) 以前的相关研究表明,CO 吸附能是描述 CO2RR 催化性能的理想值。(44,45) 虽然选择单一值作为评价标准和目标特征无疑会导致结果有缺陷,但我们工作的初衷是以较低的研究成本初步有效地筛选新的催化剂。因此,为了在准确性和消耗之间取得平衡,只考虑重要特征是合理的。

Selection and Optimization of Features
特征的选择和优化

An ideal feature set is entrusted with the function of building good models and extracting physical rules from models. In our work, we considered three criteria for selecting suitable features: (1) Features should be easily accessible. (2) Since the catalytic activity is generally dependent on the electronic structure, features should relate to it. (3) Features can simply describe the geometry of the catalysts. We took no account of features that need DFT computations; thus, feature engineering of our study is more circumspect. This consideration for feature selection is acceptable due to a sharp reduction in research cost of structure optimization before prediction. We chose the number of d electrons of metal atoms (d), electron affinity of metal atoms (E_affi_M), Pauling electronegativity of metal atoms (E_M), first ionization energy of metal atoms (1E_M), atomic number of metal atoms (M_Z), the number of nonmetal atoms (n_NM), average electron affinity of nonmetal atoms (E_affi_NM), average Pauling electronegativity of nonmetal atoms (E_aNM), average covalent radius of nonmetal atoms (NM_acov), and average atomic number of nonmetal atoms (NM_aZ) as the initial features. Simple features such as atomic number were directly gained from materials. Electronic property features were obtained from the databases of NIST. However, some structural features which need DFT computations were excluded; thus, the final model can be used without geometric optimization for designed materials. For further describing the relationship between different atoms, the initial feature set should be extended. Features in the initial feature set were combined by simple mathematical operation. Composite features, such as E_aNM-E_M (value of the difference of electronegativity between metal and nonmetal atoms) and M_cov+NM_acov (value of the sum of covalent radius of metal atoms and average atomic number of nonmetal atoms), were added to a more complex feature set. Before the training process, the performance of the feature set is indeterminate. Excessive features will lead to redundant problems, and inadequate features cannot meet the basic demand of model establishment. In order to optimize the feature set, we analyzed the correlation between every two features by Pearson correlation coefficient heat map and the rank of feature importance. The color of each small square of the heat maps shows the correlation of feature pairs. Moreover, the rank of feature importance can clearly distinguish important features from the feature set. Thus, combining these two criteria, it is convenient to gain independent features and can promote the establishment of models.
一个理想的特征集被赋予了构建良好模型和从模型中提取物理规则的功能。在我们的工作中,我们考虑了选择合适的特征的三个标准:(1) 特征应该易于访问。(2) 由于催化活性通常取决于电子结构,因此特征应与其相关。(3) 特征可以简单地描述催化剂的几何形状。我们没有考虑需要 DFT 计算的特征;因此,我们研究的特征工程更加谨慎。这种对特征选择的考虑是可以接受的,因为在预测之前结构优化的研究成本急剧降低。我们选择了金属原子的 d 电子数 (d)、金属原子的电子亲和力 (E_affi_M)、金属原子的鲍林电负性 (E_M)、金属原子的第一电离能 (1E_M)、金属原子的原子序数 (M_Z)、非金属原子数 (n_NM)、非金属原子的平均电子亲和力 (E_affi_NM), 非金属原子的平均鲍林电负性 (E_aNM)、非金属原子的平均共价半径 (NM_acov) 和非金属原子的平均原子序数 (NM_aZ) 作为初始特征。原子序数等简单特征是直接从材料中获得的。电子属性特征是从 NIST 的数据库中获得的。然而,一些需要 DFT 计算的结构特征被排除在外;因此,最终模型可以在不对设计材料进行几何优化的情况下使用。为了进一步描述不同原子之间的关系,应扩展初始特征集。初始特征集中的特征通过简单的数学运算进行组合。 复合特征,如 E_aNM-E_M (金属和非金属原子之间的电负性差值) 和 M_cov+NM_acov (金属原子的共价半径之和与非金属原子的平均原子序数的值) 被添加到更复杂的特征集中。在训练过程之前,特征集的性能是不确定的。特征过多会导致冗余问题,特征不足无法满足模型建立的基本需求。为了优化特征集,我们通过 Pearson 相关系数热图和特征重要性等级分析了每两个特征之间的相关性。热图的每个小方块的颜色显示特征对的相关性。此外,特征重要性的等级可以清楚地区分重要特征和特征集。因此,结合这两个标准,可以方便地获得独立的特征,并可以促进模型的建立。

Model Training and Performance
模型训练和性能

Different machine learning methods show different prediction performances even if the same feature set and training data are used. In this study, we mainly used the XGBR algorithm to build the model. KNR, RFR, SVR, and GBR were chosen as training algorithms to build prediction models for comparing and gaining the most suitable model. We also used a complicated machine learning method produced by TPOT, which can analyze thousands of possible combinations to find the best one for the models and parameters by genetic algorithm. Model selection and parameter adjustment of TPOT are totally automated. However, this complex and automation algorithm has two sides. One is that the algorithm has high prediction accuracy, and the other is that the multilayer structure of TPOT models makes the further adjustment of superparameters a challenge, namely, the improvement of TPOT models is out of control and restricted. Thus, we would not further improve this method in the following. Our data set (including DFT computational data of 171 designed SACs) was randomly separated to training set (80%) and testing set (20%), which is the best split ratio for most algorithms. The model performance figures of other ratios are shown in Figure S2. After the first training process, among all the trained models, the model trained by the XGBR algorithm showed the best performance. However, much credit of the successful establishment of this model must be attributed to the feature set optimization. The feature set might restrict the prediction accuracy when all the composite features were applied to train models, and cumbersome feature sets are wasteful. The model trained by the feature set including all the single features and mathematic-transformation composited features shows a prediction accuracy of r2 = 0.902 and RMSE = 0.1652. Some composite features have good independence, such as d2 and E_affi_M2. However, in the heat map, there are also some composite features analogous to corresponding single features. Thus, we removed the lowest ranking and miscellaneous features, such as E_M2, d2, NM_acov2, and 1E_M2. To find the minimum of the number of features, we trained the model by the simplest feature set which only included independent and low-correlation coefficient features. The prediction performance of this model does not have a superiority performance score (r2 = 0.797, RMSE = 0.2376), which may be due to the incomplete description of target values by inadequate features. Comparison Pearson correlation coefficient heat maps and feature importance rank of those two feature sets are shown in Figures S3 and S4. The prediction performances of these feature sets are shown in Figure S5. Then we eliminated the features of low importance and poor independence one by one, so as to reduce the redundant features and improve the prediction accuracy at the same time. Features were filtered step by step until the final feature set emerged with suitable size and provided assurance for model accuracy. By removing features without contribution from the biggest feature set, we gained the final feature set. Models built by the final feature set maintain the same r2 (0.902) and RMSE (0.1652 eV) as the biggest feature set. The 5-fold cross validation results are presented in Table S2. The Pearson correlation coefficient heat map and the feature importance rank of the final feature set are shown in Figure 2a,b, and the final selected feature set demonstrates excellent adaptability to our data set. The comparison of DFT data and predicted results by the XGBR model is shown in Figure 2c, and comparison figures of other algorithm models are presented in Figure 3. After training by the XGBR algorithm, we successfully gained an acceptable prediction model for this kind of SACs. Other models show inferior prediction accuracy to the XGBR model, and the closest models are RFR and GBR. KNR and SVR models have unsatisfactory prediction performances because of low r2 score and high RMSE value.
即使使用相同的特征集和训练数据,不同的机器学习方法也会表现出不同的预测性能。在本研究中,我们主要使用 XGBR 算法来构建模型。选择 KNR 、 RFR 、 SVR 和 GBR 作为训练算法来构建预测模型,以比较和获得最合适的模型。我们还使用了 TPOT 生成的复杂机器学习方法,该方法可以通过遗传算法分析数千种可能的组合,以找到最适合模型和参数的组合。TPOT 的模型选择和参数调整是完全自动化的。但是,这种复杂的自动化算法有两个方面。一是算法预测精度高,二是 TPOT 模型的多层结构使得超参数的进一步调整成为挑战,即 TPOT 模型的改进失控和受限。因此,我们在下面不会进一步改进这种方法。我们的数据集(包括 171 个设计 SAC 的 DFT 计算数据)被随机分为训练集 (80%) 和测试集 (20%),这是大多数算法的最佳拆分比。其他比率的模型性能数据如图 S2 所示。经过第一次训练过程后,在所有训练的模型中,XGBR 算法训练的模型表现出最佳性能。然而,成功建立此模型的大部分功劳必须归功于功能集优化。当所有复合特征都应用于训练模型时,特征集可能会限制预测准确性,而繁琐的特征集会浪费。 由特征集(包括所有单个特征和数学变换复合特征)训练的模型显示预测精度为 r2 = 0.902 和 RMSE = 0.1652。一些复合特征具有良好的独立性,例如 d2 和 E_affi_M2。但是,在热图中,也有一些类似于相应的单个特征的复合特征。因此,我们删除了排名最低的杂项特征,例如 E_M∧2、d2、NM_acov2 和 1E_M2。为了找到特征数量的最小值,我们用最简单的特征集训练了模型,该特征集只包括独立和低相关系数的特征。该模型的预测性能没有优越性性能得分 (r2 = 0.797, RMSE = 0.2376),这可能是由于特征不足对目标值的描述不完整。比较:这两个特征集的 Pearson 相关系数、热图和特征重要性排名如图 S3 和 S4 所示。这些特征集的预测性能如图 S5 所示。然后,我们逐一剔除低重要性和独立性差的特征,从而减少冗余特征,同时提高预测精度。逐步筛选特征,直到最终的特征集出现合适的大小,并为模型准确性提供保证。通过从最大的特征集中删除没有贡献的特征,我们获得了最终的特征集。由最终特征集构建的模型保持与最大特征集相同的 r2 (0.902) 和 RMSE (0.1652 eV)。 表 S2 中显示了 5 倍交叉验证结果。 尔逊相关系数热图和最终特征集的特征重要性排名如图 2a、b 所示,最终选择的特征集展示了对我们数据集的出色适应性。DFT 数据与 XGBR 模型预测结果的比较如图 2c 所示,其他算法模型的比较图如图 3 所示。经过 XGBR 算法的训练,我们成功地获得了这种 SAC 的可接受预测模型。其他模型的预测精度不如 XGBR 模型,最接近的模型是 RFR 和 GBR。由于 r2 分数低,RMSE 值高,KNR 和 SVR 模型的预测性能不令人满意。

Figure 2  图 2

Figure 2. (a) Pearson correlation coefficient matrix heat maps of the best feature set for ΔGCO prediction. (b) Feature importance rank for the best feature set. (c) Prediction results of XGBR model vs DFT data.
图 2.(a) ΔGCO 预测最佳特征集的 Pearson 相关系数矩阵热图。(b) 最佳特征集的特征重要性排名。(c) XGBR 模型与 DFT 数据的预测结果。

Figure 3  图 3

Figure 3. Prediction performance of other models trained by different ML methods: (a) KNR, (b) RFR, (c) TPOT, (d) SVR, and (e) GBR.
图 3.由不同 ML 方法训练的其他模型的预测性能:(a) KNR、(b) RFR、(c) TPOT、(d) SVR 和 (e) GBR。

Screening of Potential Candidates by the Prediction Model
通过预测模型筛选潜在候选人

After gaining the prediction model, we can use it to efficiently search potential SACs for CO2 electroreduction by predicting ΔGCO. Some designed structures are presented in Figure 4, and Figure 5 illustrates the heat map of ΔGCO for 1060 designed SAC materials consisting of 20 transition metal atoms and 53 different nonmetal bonded environments. The results were predicted on the basis of the feature set that we determined above. From the heat map in Figure 5, potential materials can be quickly filtered by distinct colors. Each number in the x axis represents a nonmetal bonded environment, and the corresponding environments can be found in Table S3. The y axis represents the kind of metal atoms. SACs represented by brown color have strong CO adsorption, indicating that the catalytic activity of subsequent reactions will be restricted. Taking the ΔGCO of available good electrocatalysts as a reference, (33,46) SACs presented by blue color have ideal ΔGCO (−0.2 ± 0.1 eV). From this heat map, if we focus on doping metal atoms, structures with Co, Fe, Ir, Ni, Os, Sc, Ti, V, Y, and Zr are included in the candidate materials. For most nonmetallic coordination environments, such as M-C2S2, M-CNO2, M-CN2P, M-N2P2, M-CNS2, M-NS3, and M-OS3 structures, there is at least one suitable SAC structure. Moreover, some materials which are screened with our prediction model have been reported, such as M-C2N2 (36) and M-N2O2. (47) For a well-defined screening, we framed all the candidate structures in red lines.
获得预测模型后,我们可以使用它通过预测 ΔGCO 来有效地搜索 CO2 电还原的潜在 SAC。4 列出了一些设计的结构,5 说明了由 20 个过渡金属原子和 53 种不同的非金属键合环境组成的 1060 种设计的 SAC 材料的 ΔGCO 热图。结果是根据我们上面确定的特征集预测的。从5 的热图中,可以按不同的颜色快速筛选潜在材料。x 轴中的每个数字代表一个非金属粘合环境,相应的环境可以在表 S3 中找到。y 轴表示金属原子的种类。以棕色为代表的 SACs 具有很强的 CO 吸附性,表明后续反应的催化活性将受到限制。以可用良好电催化剂的 ΔGCO 为参考,(33,46) 蓝色表示的 SAC 具有理想的 ΔGCO (-0.2 ± 0.1 eV)。从这张热图中可以看出,如果我们专注于掺杂金属原子,候选材料中包括了 Co、Fe、Ir、Ni、Os、Sc、Ti、V、Y 和 Zr 的结构。对于大多数非金属配位环境,例如 M-C2S2、M-CNO2、M-CN2P、M-N2P2、M-CNS2、M-NS3 和 M-OS3 结构,至少有一个合适的 SAC 结构。此外,已经报道了一些用我们的预测模型筛选的材料,例如 M-C2N2 (36) 和 M-N2O2。(47) 为了进行定义明确的筛选,我们将所有候选结构框在红线中。

Figure 4  图 4

Figure 4. Structures M-C2S2 and M-CNO2 as the designed SACs. Gray, navy blue, red, yellow, and shallow blue balls represent C, N, O, S, and transition metal atoms, respectively.
图 4.结构 M-C2S2 和 M-CNO2 作为设计的 SAC。灰色、海军蓝、红色、黄色和浅蓝色球分别代表 C、N、O、S 和过渡金属原子。

Figure 5  图 5

Figure 5. Prediction heat map of ΔGCO for designed single-atom catalysts.
图 5.设计的单原子催化剂的 ΔGCO 预测热图。

As is well known, HER is a possible concomitant reaction during CO2 electroreduction. Thus, we also applied our machine learning method to evaluate the electrocatalytic activity of all designed materials for HER. By using the adsorption Gibbs free energy change of H (ΔGH) as the prediction target, (48) we gained an acceptable prediction model after sorting out DFT data and selecting appropriate feature sets and machine learning algorithms. To gain an accurate and widely applicable model, we added more features, atomic number of metal atoms (M_Z), atomic radius of metal and nonmetal atoms (ar_M, ar_NM1, ar_NM2, ar_NM3, ar_NM4), d orbital occupancy (doo_M), and d band center of metal atoms (TM_d_band_center) which was calculated based on the data of MP (Materials Project) database, to our initial feature set. The prediction performances of different data split ratios and machine learning algorithms are presented in Figures S6 and S7. The best prediction model was trained by by XGBR algorithm with the 0.85:0.15 data split ratio. Figure S8 presents the Pearson correlation coefficient and feature importance of this final selected feature set. For HER, the absolute value of ΔGH should be close to 0, which means that the HER process is prone to appear in catalytic reactions. (49) The prediction results of ΔGH are also shown in Figure S9. The range of 0 ± 0.1 eV is presented by red color in the heat map of ΔGH. From the results of HER catalytic activity, we can further filter our designed materials. Because the mixture of different colors will affect our resolution, we used an overlapping method to gain comprehensive analysis of the final potential materials. First, we overlapped the two prediction heat maps. Because the mixed color of blue and red is purple, if the candidate CO2 electroreduction catalyst presented by blue squares turns into purple ones, it means that the catalyst also has good catalytic activity for HER. In Figure 6, if the structure squares in the red frame do not show purple color, the structures are the final choice. For example, Co-CS3, Fe–C2S2, Ni–C2NP, Sc-CN3, Ti–C2S2, V-NP3, and Zr-CN2S are considered to have good CO2 electroreduction catalytic performance.
众所周知,HER 是 CO2 电还原过程中可能发生的伴随反应。因此,我们还应用了我们的机器学习方法来评估所有为 HER 设计的材料的电催化活性。以吸附吉布斯自由能变化 H (ΔGH)作为预测目标,(48) 在整理 DFT 数据并选择合适的特征集和机器学习算法后,我们得到了一个可接受的预测模型。为了获得准确且应用广泛的模型,我们增加了更多特征,金属原子的原子序数 (M_Z)、金属和非金属原子的原子半径 (ar_M、ar_NM1、ar_NM2、ar_NM3、ar_NM4)、d 轨道占用度 (doo_M) 和金属原子的 d 波段中心 (TM_d_band_center) 这些都是根据 MP (Materials Project) 数据库的数据计算的, 添加到我们的初始功能集。 图 S6 和 S7 显示了不同数据拆分比率和机器学习算法的预测性能。最佳预测模型采用 XGBR 算法训练,数据分割比为 0.85:0.15。 图 S8 显示了最终选择的特征集的 Pearson 相关系数和特征重要性。对于 HER,ΔGH 的绝对值应接近 0,这意味着 HER 过程容易出现在催化反应中。(49) ΔGH 的预测结果也显示在图 S9 中。0 ± 0.1 eV 的范围在 ΔGH 的热图中用红色表示。根据 HER 催化活性的结果,我们可以进一步过滤我们设计的材料。 因为不同颜色的混合会影响我们的分辨率,所以我们使用了重叠方法来全面分析最终的潜在材料。首先,我们将两个预测热图重叠。由于蓝色和红色的混合色是紫色,如果蓝色方块呈现的候选 CO2 电还原催化剂变成紫色,则说明该催化剂对 HER 也具有良好的催化活性。在6 中,如果红框中的结构方块没有显示紫色,则结构是最终选择。例如,Co-CS3、Fe-C2S2、Ni-C2NP、Sc-CN3、Ti-C2S2、V-NP3 和 Zr-CN2S 被认为具有良好的 CO2 电还原催化性能。

Figure 6  图 6

Figure 6. Overlapped prediction heat maps of ΔGCO and ΔGH of designed SACs.
图 6.设计 SAC 的 ΔGCO 和 ΔGH 的重叠预测热图。

To verify our predicted potential electrocatalysts, we randomly selected three designed materials, Sc-C2O2, Sc-CN2O, and Y-CN2O for further DFT computations, and checked the stability of these candidate materials through AIMD simulations. The AIMD results are shown in Figure S10, after a simulation at 600 K for 10 ps, the structures are intact and it demonstrates that these materials are stable. Moreover, we also compared the predicted ΔGCO and the DFT computed ΔGCO of these candidates. The prediction values of Sc-C2O2, Sc-CN2O, and Y-CN2O are −0.184 eV, −0.185 eV, and −0.145 eV, respectively, and the DFT computed ones are −0.359 eV, −0.380 eV, and −0.345 eV, respectively. The results demonstrate the catalytic activity of Sc-C2O2, Sc-CN2O, and Y-CN2O.
为了验证我们预测的电位电催化剂,我们随机选择了三种设计的材料 Sc-C2O2 、 Sc-CN2O 和 Y-CN2O 进行进一步的 DFT 计算,并通过 AIMD 模拟检查了这些候选材料的稳定性。AIMD 结果如图 S10 所示,在 600 K 下模拟 10 ps 后,结构完好无损,表明这些材料是稳定的。此外,我们还比较了这些候选物的预测 ΔGCO 和 DFT 计算的 ΔGCO。Sc-C2O2、Sc-CN2O 和 Y-CN2O 的预测值分别为 −0.184 eV、−0.185 eV 和 −0.145 eV,DFT 计算值分别为 −0.359 eV、−0.380 eV 和 −0.345 eV。结果表明 Sc-C2O2 、 Sc-CN2O 和 Y-CN2O 的催化活性。
In fact, after collecting the feature data from NIST, we could get prediction results within less than a minute, which greatly reduces the research cost compared with the general high-throughput methods, and the prediction results can indubitably be used as a reference for further experiments of novel CO2 electroreduction catalysts.
事实上,从 NIST 收集特征数据后,我们可以在不到一分钟的时间内获得预测结果,与一般的高通量方法相比,这大大降低了研究成本,预测结果无疑可以作为新型 CO2 电还原催化剂进一步实验的参考。

Conclusions  结论

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Overall, by using a DFT database and machine learning methods, we successfully established a prediction model that can fast explore potential catalysts for CO2 electroreduction. After building the DFT database, feature engineering and model training, the final XGBR model shows the best prediction accuracy. By using this model, 1060 designed SACs including combinations of 20 transition metal atoms and 53 different nonmetal bonded environments were used as our prediction target materials. Moreover, the catalytic activity of HER of 1060 designed materials was also predicted by machine learning. By combining the prediction results of CO2 electroreduction and HER, we rapidly screened suitable materials by overlapping the results, and then 94 potential SACs for CO2 eletroreduction were proposed, which greatly improves the efficiency compared with high-throughput methods. The framework of establishment of prediction models is an excellent tool for preliminary investigations. Before further more accurate investigations, researchers can quickly find potential materials from the predicted heat maps. Considering the current huge demand for energy resources, the effective screening method with machine learning algorithms in this work can promote energy-related developments.
总体而言,通过使用 DFT 数据库和机器学习方法,我们成功建立了一个预测模型,可以快速探索 CO2 电还原的潜在催化剂。在构建 DFT 数据库、特征工程和模型训练后,最终的 XGBR 模型显示出最佳的预测精度。通过使用该模型,使用 1060 个设计的 SAC,包括 20 个过渡金属原子和 53 种不同的非金属键合环境的组合,作为我们的预测目标材料。此外,机器学习还预测了 1060 种设计材料的 HER 的催化活性。结合 CO2 电还原和 HER 的预测结果,通过重叠结果快速筛选出合适的材料,然后提出了 94 种潜在的 CO2 电还原 SAC,与高通量方法相比,效率大大提高。建立预测模型的框架是初步调查的极好工具。在进一步进行更准确的调查之前,研究人员可以从预测的热图中快速找到潜在的材料。考虑到当前对能源资源的巨大需求,本研究中采用机器学习算法的有效筛选方法可以促进能源相关发展。

Supporting Information  支持信息

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.0c05964.
支持信息可在 https://pubs.acs.org/doi/10.1021/acs.jpcc.0c05964 免费获取。

  • Figures of SAC structures of the training data set; CO/H adsorption energy prediction results of the comparison between the XGBR model and DFT computed data with different data split ratios and different feature sets; the Pearson correlation coefficient heat maps and feature importance ranks of different feature sets for CO/H adsorption energy prediction model; H adsorption prediction models trained by different algorithms; AIMD results of some predicted SACs; parameters of the final machine learning models; cross validation results of models; and the order number in the heat map of different kinds of structures (PDF)
    训练数据集的 SAC 结构图;XGBR 模型与不同数据分割比、不同特征集的 DFT 计算数据对比的 CO/H 吸附能量预测结果;CO/H 吸附能预测模型的 Pearson 相关系数热图和不同特征集的特征重要性排名;不同算法训练的 H 吸附预测模型;一些预测的 SAC 的 AIMD 结果;最终机器学习模型的参数;模型的交叉验证结果;以及不同类型结构的热图 (PDF) 中的订单号

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Author Information  作者信息

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  • Corresponding Authors  通讯作者
    • Xu Zhang - School 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, P. R. China Email: zhangxu@nankai.edu.cn
      张旭 - 南开大学材料科学与工程学院,新能源材料化学研究所,可再生能源转换与储存中心(ReCast),先进能源材料化学教育部重点实验室,天津,300350 电子邮件: zhangxu@nankai.edu.cn
    • Zhen Zhou - School 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, P. R. ChinaOrcidhttp://orcid.org/0000-0003-3232-9903 Email: zhouzhen@nankai.edu.cn
      周震 - 南开大学材料科学与工程学院,新能源材料化学研究所,可再生能源转换与储存中心(ReCast),先进能源材料化学教育部重点实验室,天津,300350 Orcid http://orcid.org/0000-0003-3232-9903 电子邮件:zhouzhen@nankai.edu.cn
  • Authors  作者
    • An Chen - School 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, P. R. China
      安晨 - 南开大学材料科学与工程学院,新能源材料化学研究所,可再生能源转换与储存中心(ReCast),先进能源材料化学教育部重点实验室,天津,300350
    • Letian Chen - School 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, P. R. China
      陈乐天 - 南开大学材料科学与工程学院,新能源材料化学研究所,可再生能源转换与储存中心(ReCast),先进能源材料化学教育部重点实验室,天津,300350
    • Sai Yao - School 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, P. R. China
      尧赛 - 南开大学材料科学与工程学院, 新能源材料化学研究所, 可再生能源转化与储存中心(ReCast), 先进能源材料化学教育部重点实验室, 天津 300350
  • Notes  笔记
    The authors declare no competing financial interest.
    作者声明没有竞争性的经济利益。

Acknowledgments  确认

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This work was supported by NSFC (21933006 and 91845112), China Postdoctoral Science Foundation (2019M660055), and the Fundamental Research Funds for the Central Universities, Nankai University (63201063).
这项工作得到了国家自然科学基金 (21933006 和 91845112)、中国博士后科学基金 (2019M660055) 和中央高校基本科研业务费 南开大学 (63201063) 的支持。

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  • Abstract

    Figure 1

    Figure 1. Structure of SAC. Shallow blue, green, and gray balls represent transition metal (Ag, Au, Co, Cu, Fe, Ir, Mn, Mo, Ni, Os, Pd, Pt, Rh, Ru, Sc, Ti, V, Y, Zn or Zr), nonmetal (C, N, O, P, or S), and C atoms, respectively.

    Figure 2

    Figure 2. (a) Pearson correlation coefficient matrix heat maps of the best feature set for ΔGCO prediction. (b) Feature importance rank for the best feature set. (c) Prediction results of XGBR model vs DFT data.

    Figure 3

    Figure 3. Prediction performance of other models trained by different ML methods: (a) KNR, (b) RFR, (c) TPOT, (d) SVR, and (e) GBR.

    Figure 4

    Figure 4. Structures M-C2S2 and M-CNO2 as the designed SACs. Gray, navy blue, red, yellow, and shallow blue balls represent C, N, O, S, and transition metal atoms, respectively.

    Figure 5

    Figure 5. Prediction heat map of ΔGCO for designed single-atom catalysts.

    Figure 6

    Figure 6. Overlapped prediction heat maps of ΔGCO and ΔGH of designed SACs.

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  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.0c05964.

    • Figures of SAC structures of the training data set; CO/H adsorption energy prediction results of the comparison between the XGBR model and DFT computed data with different data split ratios and different feature sets; the Pearson correlation coefficient heat maps and feature importance ranks of different feature sets for CO/H adsorption energy prediction model; H adsorption prediction models trained by different algorithms; AIMD results of some predicted SACs; parameters of the final machine learning models; cross validation results of models; and the order number in the heat map of different kinds of structures (PDF)


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