机器学习辅助筛选SA-FLP双活性位催化剂用于甲烷与水共转化制甲醇反应

doi:10.1016/S1872-2067(24)60225-1

催化学报 ›› 2025, Vol. 70: 311-321.DOI: 10.1016/S1872-2067(24)60225-1

机器学习辅助筛选SA-FLP双活性位催化剂用于甲烷与水共转化制甲醇反应

班涛^a^,^b, 王建伟^a, 余希阳^b, 田海阔^b, 高新^b, 黄正清^b, 常春然^b^,^*()

^a新疆大学化工学院, 新疆煤炭清洁转化与化工自治区重点实验室, 新疆乌鲁木齐 830017
^b西安交通大学化学工程与技术学院, 陕西省能源化工过程强化重点实验室, 陕西西安 710049

收稿日期:2024-10-15 接受日期:2024-12-17 出版日期:2025-03-18 发布日期:2025-03-20
通讯作者: * 电子信箱: changcr@mail.xjtu.edu.cn (常春然).
基金资助:
国家自然科学基金(22078257);国家自然科学基金(U23A20112);国家重点研发计划(2023YFA1506300);陕西省秦创原“科学家+工程师”队伍建设(2023KXJ-276);陕西北元化工集团有限公司科研项目(2023413611014);陕西省青年人才支持计划; 陕西省技术创新团队(2024RS-CXTD-47);中科院青年跨学科团队; 高等学校学科创新引智计划(B23025);国家自然科学基金单原子催化中心(22388102);新疆维吾尔自治区自然科学基金(2024D01C262);陕西省自然科学基础研究计划(2024JC-YBQN-0071);太原理工大学清洁高效煤燃烧国家重点实验室(MJNYSKL202309)

Machine learning-assisted screening of SA-FLP dual-active-site catalysts for the production of methanol from methane and water

Tao Ban^a^,^b, Jian-Wei Wang^a, Xi-Yang Yu^b, Hai-Kuo Tian^b, Xin Gao^b, Zheng-Qing Huang^b, Chun-Ran Chang^b^,^*()

^aKey Laboratory of Coal Cleaning Conversion and Chemical Engineering Process, School of Chemical Engineering and Technology, Xinjiang University, Urumqi 830017, Xinjiang, China
^bShaanxi Key Laboratory of Energy Chemical Process Intensification, School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China

Received:2024-10-15 Accepted:2024-12-17 Online:2025-03-18 Published:2025-03-20
Contact: * E-mail: changcr@mail.xjtu.edu.cn (C.-R. Chang).
Supported by:
National Natural Science Foundation of China(22078257);National Natural Science Foundation of China(U23A20112);National Key R&D Program of China(2023YFA1506300);Qinchuangyuan "Scientists + Engineers" Team Construction Program of Shaanxi Province(2023KXJ-276);the research program from Shaanxi Beiyuan Chemical Industry Group Co., Ltd.(2023413611014);C. R. C. acknowledges the Young Talent Support Plan of Shaanxi Province, the Shaanxi Technological Innovation Team(2024RS-CXTD-47);CAS Youth Interdisciplinary Team, the Programme of Introducing Talents of Discipline to Universities(B23025);NSFC Center for Single-Atom Catalysis(22388102);Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01C262);X.G. acknowledges the financial support from Natural Science Basic Research Program of Shaanxi Province at China(2024JC-YBQN-0071);Foundation of State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, China(MJNYSKL202309)

摘要/Abstract

摘要：

在温和条件下实现甲烷一步法直接转化为甲醇是多相催化领域的一项重大挑战. 当前常用氧化剂如SO₃, H₂O₂和N₂O虽能促进反应, 但其高昂的成本限制了工业应用; 而低成本氧气作为氧化剂则会导致副产物CO₂生成, 降低甲醇的选择性. 利用水作为氧化剂是一种潜在的解决方案, 但在标准条件下, 甲烷与水直接转化为甲醇的反应热力学受限, 且现有金属掺杂分子筛催化剂产率较低. 因此, 亟需开发新型高效催化剂以实现甲烷与水的协同转化. 本文结合密度泛函理论(DFT）计算与机器学习(ML)方法,提出并设计了“单原子-受阻路易斯酸碱对” (SA-FLP)双活性位催化剂, 为甲烷与水直接转化为甲醇提供了新的催化设计思路与理论支撑.

本文设计了9种SA-FLP双活性位催化剂, 系统地研究了甲烷和水在单原子(SA)和受阻路易斯酸碱对(FLP)位点的活化行为, 以及甲烷和水共转化制甲醇的反应机理, 并构建了微观动力学模拟模型. 通过将甲醇生成的转化频率(TOF)与SA-FLP催化剂的物理化学性质关联, 进一步构建了符合需求的机器学习模型, 用于预测16种尚未开发的SA-FLP催化剂的甲醇生成TOF数据. DFT计算结果表明, 设计的SA-FLP催化剂具有独特的双活性位点结构, 能够高效地活化甲烷和水分子. 其中, SA位点主要促进甲烷的C−H键活化, 而FLP位点则有助于水分子的O−H键解离, 距离适中的SA-FLP耦合位点则有效促进CH₃*与OH*之间的C−O偶联, 从而生成甲醇. 微观动力学模拟结果表明, Co₁-FLP和Pt₁-FLP催化剂分别实现了1.01 × 10⁻³ s⁻¹和8.80 × 10⁻⁴ s⁻¹的甲醇生成TOF, 高于其他7种SA-FLP催化剂2−4个数量级, 并且远高于实验中报道值(7.53 × 10⁻⁷−1.74 × 10⁻⁶ s⁻¹), 展示出较好的催化活性. 为加速催化剂的筛选进程, 本文结合DFT计算结果和ML方法, 基于13个简单描述符构建了梯度提升回归模型. 结果显示, 该模型对甲醇生成TOF预测具有较高的准确性, 均方根误差低至0.009 s⁻¹, 相关性系数接近于1.00. 通过ML模型的进一步预测, 发现Fe₁-FLP, Mn₁-FLP, Ti₁-FLP和V₁-FLP四种催化剂具有优异的甲醇生成活性, 其TOFs均超过现有实验最佳水平, 显示出这些催化剂在甲烷与水共转化制甲醇反应中的巨大应用潜力.

综上所述, 本文成功开发了一种结合DFT与ML设计和筛选高效的SA-FLP催化剂的新策略, 为甲烷与水直接转化制甲醇提供了理论基础和新型催化剂设计思路. 该研究不仅拓展了催化剂设计方法, 也为解决甲烷直接高效转化难题提供了重要的科学依据.

关键词: 单原子催化剂, 受阻路易斯酸碱对, 机器学习, 双活性位点, 甲醇合成

Abstract:

One-step direct production of methanol from methane and water (PMMW) under mild conditions is challenging in heterogeneous catalysis owing to the absence of highly effective catalysts. Herein, we designed a series of “Single-Atom” - “Frustrated Lewis Pair” (SA-FLP) dual active sites for the direct PMMW via density functional theory (DFT) calculations combined with a machine learning (ML) approach. The results indicate that the nine designed SA-FLP catalysts are capable of efficiently activate CH₄ and H₂O and facilitate the coupling of OH* and CH₃* into methanol. The DFT-based microkinetic simulation (MKM) results indicate that CH₃OH production on Co₁-FLP and Pt₁-FLP catalysts can reach the turnover frequencies (TOFs) of 1.01 × 10⁻³ s^-1 and 8.80 × 10⁻⁴ s^-1, respectively, which exceed the experimentally reported values by three orders of magnitude. ML results unveil that the gradient boosted regression model with 13 simple features could give satisfactory predictions for the TOFs of CH₃OH production with RMSE and R² of 0.009 s^-1 and 1.00, respectively. The ML-predicted MKM results indicate that four catalysts including V₁-, Fe₁-, Ti₁-, and Mn₁-FLP exhibit higher TOFs of CH₃OH production than the value that the most relevant experiments reported, indicating that the four catalysts are also promising catalysts for the PMMW. This study not only develops a simple and efficient approach for design and screening SA-FLP catalysts but also provides mechanistic insights into the direct PMMW.

Key words: Single-atom catalyst, Frustrated Lewis pair, Machine learning, Dual active sites, Methanol synthesis

班涛, 王建伟, 余希阳, 田海阔, 高新, 黄正清, 常春然. 机器学习辅助筛选SA-FLP双活性位催化剂用于甲烷与水共转化制甲醇反应[J]. 催化学报, 2025, 70: 311-321.

Tao Ban, Jian-Wei Wang, Xi-Yang Yu, Hai-Kuo Tian, Xin Gao, Zheng-Qing Huang, Chun-Ran Chang. Machine learning-assisted screening of SA-FLP dual-active-site catalysts for the production of methanol from methane and water[J]. Chinese Journal of Catalysis, 2025, 70: 311-321.

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链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(24)60225-1

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图/表 10

Fig. 1. (a) The optimized CeO2(110). (b) The optimized M1@CeO2. (c) The optimized M1@CeO2-Vo. In (a), the black circle indicates the position where a Ce atom is substituted by a transition metal atom, while in (b), the blue circle marks the site where an O atom has been removed.

Fig. 2. (a) Eads of H2O at SA and FLP sites. (b) Typical adsorption configurations of H2O at SA and FLP sites.

Fig. 3. (a) Eads of CH4 on SA site. (b) Typical adsorption configurations of CH4 adsorption and dissociation with pre-adsorbed H2O. (c) Reaction and activation energy profiles for the adsorption and activation of CH4.

Fig. 4. (a) The potential energy profile for the PMMW on the nine SA-FLP catalysts. The zero-energy reference is defined as the sum of the energies of SA-FLP surface, H2O(g), and CH4(g). (b) Representative images of the PMMW.

Fig. 5. TOFs for CH3OH production varying with pressure and temperature for Co1-FLP (a), Ni1-FLP (b), Cu1-FLP (c), Rh1-FLP (d), Pd1-FLP (e), Ag1-FLP (f), Ir1-FLP (g), Pt1-FLP (h), and Au1-FLP (i), with the initial CH4/H2O ratio set to 1.

Fig. 6. Pearson correlation analysis corresponds to the 17 descriptors used in this study. Values below 0.01 are displayed as zero.

Table 1 The RMSE and R2 for all the evaluated ML models for both the test set and cross-validation, respectively.

Model	Test set		Cross-validation
Model	R²	RMSE (s^-1)	R²	RMSE (s^-1)
SVM	0.999	0.072	0.999	0.072
RFR	1.00	0.030	1.00	0.028
GBR	1.00	0.010	1.00	0.011
DTR	1.00	0.036	1.00	0.027
ETR	1.00	0.037	1.00	0.033
KNN	1.00	0.037	1.00	0.028
LASSO	0.877	1.051	0.875	1.052
OLR	0.988	0.329	0.988	0.326
PLS	0.988	0.329	0.988	0.326

Fig. 7. MKM-simulated CH3OH TOF vs ML-predicted CH3OH TOF on the LASSO model (a), PLS model (b), OLR model (c), SVM model (d), ETR model (e), RFR model (f), KNN model (g), DTR model (h), and GBR model (i).

Fig. 8. (a) Permutation feature importance score in the GBR model. (b) MKM-simulated CH3OH TOF vs. ML-predicted CH3OH TOF on the GBR model with 10-fold cross-validation.

Fig. 9. The ML-predicted CH3OH TOFs varying with pressure and temperature for Ti1-FLP (a), V1-FLP (b), Mn1-FLP (c), Fe1-FLP (d), Zn1-FLP (e), Zr1-FLP (f), Nb1-FLP (g), Mo1-FLP (h), Tc1-FLP (i), Ru1-FLP (j), Cd1-FLP (k), Hf1-FLP (l), Ta1-FLP (m), W1-FLP (n), Re1-FLP (o), Os1-FLP (p), with the initial CH4/H2O ratio set to 1.

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机器学习辅助筛选SA-FLP双活性位催化剂用于甲烷与水共转化制甲醇反应

Machine learning-assisted screening of SA-FLP dual-active-site catalysts for the production of methanol from methane and water

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