机器学习辅助发现具有超高初始选择性的甲醇制烯烃分子筛催化剂

doi:10.1016/S1872-2067(25)64903-5

催化学报 ›› 2026, Vol. 81: 124-135.DOI: 10.1016/S1872-2067(25)64903-5

机器学习辅助发现具有超高初始选择性的甲醇制烯烃分子筛催化剂

王心怡^a^,^d^,¹, 王朝旗^b^,¹, 杨淼^a(), 王晓光^b(), 左越中^b, 张壮壮^b, 吴一墨^a^,^e, 韩晶峰^a, 李冰^a, 黄玮^c, 任利敏^c, 魏迎旭^a, 刘欣梅^d, 田鹏^a, 刘中民^a^,^e()

^a 中国科学院大连化学物理研究所, 低碳催化技术国家工程研究中心, 辽宁大连 116023
^b 大连理工大学数学科学学院, 辽宁大连 116024
^c 大连理工大学化工学院, 精细化工国家重点实验室, 辽宁大连 116024
^d 中国石油大学(华东)化学化工学院, 重质油国家重点实验室, 山东青岛 266580
^e 中国科学院大学, 北京 100049

收稿日期:2025-08-15 接受日期:2025-09-22 出版日期:2026-02-18 发布日期:2025-12-26
通讯作者: *电子信箱: yangmiao@dicp.ac.cn (杨淼),wangxg@dlut.edu.cn (王晓光),liuzm@dicp.ac.cn (刘中民).
作者简介:¹共同第一作者.
基金资助:
国家重点研发计划(2024YFE0207000);国家自然科学基金(22171259);国家自然科学基金(12461050);国家自然科学基金(21991090);国家自然科学基金(21991091);国家自然科学基金(22288101);国家自然科学基金(22372020);榆林中科洁净能源创新研究院人工智能科技专项资助(DNL-YL A202206)

Machine-learning-aided discovery of methanol-to-olefins zeolite catalysts with ultra-high initial selectivity

Xinyi Wang^a^,^d^,¹, Chaoqi Wang^b^,¹, Miao Yang^a(), Xiaoguang Wang^b(), Yuezhong Zuo^b, Zhuangzhuang Zhang^b, Yimo Wu^a^,^e, Jingfeng Han^a, Bing Li^a, Wei Huang^c, Limin Ren^c, Yingxu Wei^a, Xinmei Liu^d, Peng Tian^a, Zhongmin Liu^a^,^e()

^a National Engineering Research Center of Lower-Carbon Catalysis Technology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China
^b School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, Liaoning, China
^c State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, Liaoning, China
^d State Key Laboratory of Heavy Oil and College of Chemistry and Chemical Engineering, China University of Petroleum, Qingdao 266580, Shandong, China
^e University of Chinese Academy of Sciences, Beijing 100049, China

Received:2025-08-15 Accepted:2025-09-22 Online:2026-02-18 Published:2025-12-26
Contact: *E-mail: yangmiao@dicp.ac.cn (M. Yang),wangxg@dlut.edu.cn (X. Wang),liuzm@dicp.ac.cn (Z. Liu).
About author:¹ These authors contributed equally to this work.
Supported by:
National Key Research and Development Program of China(2024YFE0207000);National Natural Science Foundation of China(22171259);National Natural Science Foundation of China(12461050);National Natural Science Foundation of China(21991090);National Natural Science Foundation of China(21991091);National Natural Science Foundation of China(22288101);National Natural Science Foundation of China(22372020);AI S&T Program of Yulin Branch, Dalian National Laboratory for Clean Energy, CAS(DNL-YL A202206)

摘要/Abstract

摘要：

随着甲醇制烯烃(MTO)工业化进程的持续推进和反应机理研究的不断深入, 设计合成优质的MTO催化剂以持续提升MTO工业过程的经济性和灵活调控烯烃产物分布已成为极具挑战的课题. 数据驱动模拟可以帮助研究者预测化学反应趋势和结果.

借助机器学习(ML)研究MTO催化问题, 有望打破以往经验桎梏, 带来反应机制和催化剂设计的新突破. 但要获得特定化学问题的具体指导策略, 仍要求研究者针对具体体系收集和整理实验数据、编码相关变量并重新训练专用模型.

本研究展示如何利用ML工作流程发现潜在的MTO分子筛催化剂. 首先建设分子筛催化MTO反应的数据库, 自变量包含分子筛结构与物性参数和反应条件等, 因变量包含甲醇转化率以及乙烯和丙烯选择性等. 其次构建自变量与因变量的关联模型. 基于数据特征训练了20多种机器学习模型、并对其准确性和可靠性进行评估和实验验证. 结果显示: 基于树类的集成学习方法在转化率(分类分析)和低碳烯烃选择性(回归分析)预测方面均表现出色, 准确率超过90%. 基于机器学习模型、开发出可用于预测分子筛MTO催化性能和最佳操作条件的软件, 方便分子筛催化剂研发. 为验证机器学习模型的泛化能力和准确性, 进一步合成了此前数据库中并未包含的STT分子筛, 使用软件预测最佳MTO评价条件及结果. 实验结果显示与预测值基本吻合, 证实了集成机器学习方法的可靠性和实用性. 从机器学习的可解释性出发, 利用决策树、特征重要性及SHAP分析, 提取获得高选择性的决策规则: 分子筛最大孔道不超过9元环(LRS≤9), 分子筛中可容纳球体的最大直径(MDi)在7.35‒7.71 Å之间. 结合较低酸密度更为理想(A/T≤0.01)的结论, 重新聚焦高硅SSZ-13 (Si/Al = 100)分子筛, 意外发现该材料在450 ºC, 空速1 h^‒1条件下展现出超高的初始低碳烯烃选择性(反应37 min时乙烯加丙烯选择性高达87.6%, 其中乙烯选择性高达61.1%), 这样高的单乙烯选择性在MTO反应中极为罕见.

综上, ML在分子筛催化剂研发方面展示出有效性和应用潜力, 它可以避开复杂的反应机理, 降低实验室试错成本, 并为解决特定催化剂问题提供新机遇. 然而, 我们也认识到, ML方法高度依赖数据质量和数量. 在有限数据情况下, 还必须结合领域专家的专业知识与判断, 才能获得创新性成果.

关键词: 甲醇制烯烃, 分子筛, 机器学习, 理论设计与合成, 预测

Abstract:

With the continuous advancement of the industrialized methanol-to-olefins (MTO) process and a profound understanding of its mechanism, designing MTO catalysts to enhance light olefin yields and flexibly regulate product distribution has emerged as a significant challenge. Data-driven modeling allows chemists to anticipate reaction trends and outcomes. However, for models to be instructive for specific chemical issues, chemists must collect experimental data, encode the relevant variables and retrain specialized models. In this work, we demonstrate how to use a machine learning (ML) workflow to discover a potential MTO zeolite catalyst. An MTO database was built, on which over 20 types of ML models were trained, followed by their evaluation and experimental validation. The decision rules for high selectivity were extracted, facilitating the targeting of potential MTO catalysts and the understanding of MTO reaction mechanism. A rapid prediction of optimal MTO evaluation conditions and results for a given zeolite catalyst was also realized, greatly saving the cost of trial and error. In particular, a special MTO catalyst with high initial ethene selectivity over 60% was found, demonstrating the effectiveness and capability of ML techniques.

Key words: Methanol to olefins, Zeolite catalyst, Machine learning, Rational design and synthesis, Prediction

王心怡, 王朝旗, 杨淼, 王晓光, 左越中, 张壮壮, 吴一墨, 韩晶峰, 李冰, 黄玮, 任利敏, 魏迎旭, 刘欣梅, 田鹏, 刘中民. 机器学习辅助发现具有超高初始选择性的甲醇制烯烃分子筛催化剂[J]. 催化学报, 2026, 81: 124-135.

Xinyi Wang, Chaoqi Wang, Miao Yang, Xiaoguang Wang, Yuezhong Zuo, Zhuangzhuang Zhang, Yimo Wu, Jingfeng Han, Bing Li, Wei Huang, Limin Ren, Yingxu Wei, Xinmei Liu, Peng Tian, Zhongmin Liu. Machine-learning-aided discovery of methanol-to-olefins zeolite catalysts with ultra-high initial selectivity[J]. Chinese Journal of Catalysis, 2026, 81: 124-135.

×
扫码分享

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(25)64903-5

https://www.cjcatal.com/CN/Y2026/V81/I2/124

图/表 9

Fig. 1. Schematic for curating the datasets: extracting structural parameters of zeolites from the IZA database, searching for MTO catalytic results of different zeolites from published papers, downloading the papers, and mining text, tables and graphs for reaction conditions and catalytic results.

Table 1 The type, name and range of the variables in the MTO database.

Variables		Name of variables/units	Range/counts
Input variables	catalyst property	Mod.	0/5463
		Mod.	1/1273
		AS	0 (SAPO)/1959
		AS	1 (zeolite)/4777
		A/T	0-1
		FD_Si/T/1000Å [3]	12.80-19.70
		LRS	8-MR/4159
			10-MR/1843
			12-MR/712
			18-MR/22
		MD_a/Å	1.27-7.35
		MD_b/Å	1.49-7.35
		MD_c/Å	1.53-11.20
		MD_i/Å	5.25-11.74
		CD	1D/436
			2D/1421
			3D/4879
		CS/µm	0.02-5.00
	reaction conditions	RT/°C	270-700
		WHSV/h^‒1	0.35-48.00
		TOS/min	0-30156.92
Output variables		methanol conversion/%	0.00-100.00
		S_ethene/%	0.00-68.61
		S_propene/%	0.00-68.90
		S_C2=+C3=/%	0.00-92.21
		P/E	0.00-37.37

Fig. 2. (a) Illustration of some inputs including AS (acid strength): 0 for SAPO and 1 for aluminosilicate; A/T (acid density): Si/T for SAPO and Al/T for aluminosilicate (T refers to the sum of framework atoms [T atoms]); Mod.: 1 for modified and 0 for unmodified. (b) The correlation heatmap of all inputs in the MTO dataset.

Fig. 3. Summary of the AUC for each ML algorithm for classification of methanol conversion. The inset is the performance of classification on the whole dataset given by the stacking classifier.

Fig. 4. Feature importance heuristic given by the DT classifier for methanol conversion and visual structure of the DT. Some of the typical leave notes mentioned have been highlighted with bule solid (with more “low” points) and red dotted line (with more “high” points) circles.

Fig. 5. The performance of 23 ML methods for SC2=+C3= (a) and P/E (b) respectively. Four metrics, MAE, RMSE, R2, and RMSLE were used to evaluate these methods. The ML methods before the gray line are better. The training and test sets of SC2=+C3= (c) and P/E (d) by Stacking Regressor.

Fig. 6. (a) The visual structure of the DT. The numbers below the points are the average values of SC2=+C3=. (b) The feature importance heuristic given by the DT Regressor for SC2=+C3=. (c) SHAP analysis for SC2=+C3=. A color gradient, ranging from blue solid line (low feature values) to pink/red dotted line (high feature values), helps visualize the correlation between feature values and their SHAP values.

Table 2 Lists of the information on materials belonging to the seven topologies.

Topology	SAPOs	A/T	Zeolites	A/T	CD
AEI	SAPO-18 [64]	0‒0.1	SSZ-39 [65]	0.1	3
AWW	AlPO-22 [66] AlPO-CJB1 [67]	0	none	—	1
AFT	AlPO-52 [68]	0	SSZ-112 [69]	~0.13	3
AFX	SAPO-56 [70,71]	0.15‒0.2	SSZ-16 [72]	0.14	3
CHA	SAPO-34 [73]	0.05‒0.2	SSZ-13 [74-76]	0.02‒0.2, 0	3
DDR	none	—	ZSM-58 [77,78]	0.01‒0.02, 0	2
SFW	none	—	SSZ-52 [79]	0.11	3

Fig. 7. (a) MTO catalytic performance of SSZ-13s with different Si/Al ratios at 450 °C, WHSV of 1.0 h?1. (b) The experimental (solid symbols) and predicted (hollow symbols) MTO catalytic performance of STT-100 at 450 °C and WHSV of 1.0 h?1. The predicted lifetime is labeled by the gray dotted line.

参考文献

[1]	G. W. Huber, S. Iborra, A. Corma, Chem. Rev., 2006, 106, 4044-4098.
[2]	B. Yilmaz, U. Müller, Top. Catal., 2009, 52, 888-895.
[3]	J. Liang, H. Li, S. Zhao, W. Guo, R. Wang, M. Ying, Appl. Catal., 1990, 64, 31-40.
[4]	M. Stöcker, Microporous Mesoporous Mater., 1999, 29, 3-48.
[5]	P. Tian, Y. Wei, M. Ye, Z. Liu, ACS Catal., 2015, 5, 1922-1938.
[6]	M. Yang, D. Fan, Y. Wei, P. Tian, Z. Liu, Adv. Mater., 2019, 31, 1902181.
[7]	X. Jiang, X. Nie, X. Guo, C. Song, J. G. Chen, Chem. Rev., 2020, 120, 7984-8034.
[8]	U. Olsbye, S. Svelle, M. Bjørgen, P. Beato, T. V. W. Janssens, F. Joensen, S. Bordiga, K. P. Lillerud, Angew. Chem. Int. Ed., 2012, 51, 5810-5831.
[9]	J. H. Kang, F. H. Alshafei, S. I. Zones, M. E. Davis, ACS Catal., 2019, 9, 6012-6019.
[10]	P. Ferri, C. Li, C. Paris, A. Vidal-Moya, M. Moliner, M. Boronat, A. Corma, ACS Catal., 2020, 9, 11542-11551.
[11]	S. Standl, O. Hinrichsen, Catalysts, 2018, 8, 626.
[12]	Q. Zhang, G. Chen, Y. Wang, M. Chen, G. Guo, J. Shi, J. Luo, J. Yu, Chem. Mater., 2018, 30, 2750-2758.
[13]	Y. Wu, J. Han, W. Zhang, Z. Yu, K. Wang, X. Fang, Y. Wei, Z. Liu, J. Am. Chem. Soc., 2024, 146, 8086-8097.
[14]	X. Zhao, L. Wang, J. Li, S. Xu, W. Zhang, Y. Wei, X. Guo, P. Tian, Z. Liu, Catal. Sci. Technol., 2017, 7, 5882-5892.
[15]	X. Zhao, L. Wang, P. Guo, N. Yan, T. Sun, S. Lin, X. Guo, P. Tian, Z. Liu, Catal. Sci. Technol., 2018, 8, 2966-2974.
[16]	M. Yoshioka, T. Yokoi, T. Tatsumi, ACS Catal., 2015, 5, 4268-4275.
[17]	Q. Sun, Z. Xie, J. Yu, Natl. Sci. Rev., 2018, 5, 542-558.
[18]	R. Martínez-Franco, Z. Li, J. Martínez-Triguero, M. Moliner, A. Corma, Catal. Sci. Technol., 2016, 6, 2796-2806.
[19]	M. Dusselier, M. E. Davis, Chem. Rev., 2018, 118, 5265-5329.
[20]	I. Pinilla-Herrero, U. Olsbye, C. Márquez-Álvarez, E. Sastre, J. Catal., 2017, 352, 191-207.
[21]	W. Song, A. H. Fu, J. F. Haw, J. Am. Chem. Soc., 2001, 123, 4749-4754.
[22]	J. F. Haw, W. Song, D. M. Marcus, J. B. Nicholas, Acc. Chem. Res., 2003, 36, 317-326.
[23]	J. Li, Y. Wei, J. Chen, P. Tian, X. Su, S. Xu, Y. Qi, Q. Wang, Y. Zhou, Y. He, Z. Liu, J. Am. Chem. Soc., 2012, 134, 836-839.
[24]	J. Li, Y. Wei, J. Chen, S. Xu, P. Tian, X. Yang, B. Li, J. Wang, Z. Liu, ACS Catal., 2015, 5, 661-665.
[25]	S. Lin, Y. Zhi, W. Chen, H. Li, W. Zhang, C. Lou, X. Wu, S. Zeng, S. Xu, J. Xiao, A. Zheng, Y. Wei, Z. Liu, J. Am. Chem. Soc., 2021, 143, 12038-12052.
[26]	M. Yang, B. Li, M. Gao, S. Lin, Y. Wang, S. Xu, X. Zhao, P. Guo, Y. Wei, M. Ye, P. Tian, Z. Liu, ACS Catal., 2020, 10, 3741-3749.
[27]	Y. Wang, J. Han, N. Wang, B. Li, M. Yang, Y. Wu, Z. Jiang, Y. Wei, P. Tian, Z. Liu, Chin. J. Catal., 2022, 43, 2259-2269.
[28]	S. Xu, Y. Zhi, J. Han, W. Zhang, X. Wu, T. Sun, Y. Wei, Z. Liu, Adv. Catal., 2017, 61, 37-122.
[29]	S. Lin, Y. Zhi, Z. Liu, J. Yuan, W. Liu, W. Zhang, Z. Xu, A. Zheng, Y. Wei, Z. Liu,, Natl. Sci. Rev., 2022, 9, nwac151.
[30]	S. Lin, Y. Zhi, W. Zhang, X. Yuan, C. Zhang, M. Ye, S. Xu, Y. Wei, Z. Liu, Chin. J. Catal., 2023, 46, 11-27.
[31]	Y. Li, H. Cao, J. Yu, ACS Nano, 2018, 12, 4096-4104.
[32]	S. Chen, L. Li, Y. Li, J. Yu, Chem. J. Chin. Univ., 2021, 42, 179-187.
[33]	M. Gao, H. Li, J. Yu, M. Ye, Z. Liu, AlChE. J., 2023, 69, e17881.
[34]	P. Raccuglia, K. C. Elbert, P. D. F. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, A. J. Norquist, Nature, 2016, 533, 73-76.
[35]	K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature, 2018, 559, 547-555.
[36]	I. W. Davies, Nature, 2019, 570, 175-181.
[37]	O. Kononova, T. He, H. Huo, A. Trewartha, E. A. Olivetti, G. Ceder, iScience, 2021, 24, 102155.
[38]	A. F. Zahrt, J. J. Henle, B. T. Rose, Y. Wang, W. T. Darrow, S. E. Denmark, Science, 2019, 363, eaau5631.
[39]	T. Toyao, Z. Maeno, S. Takakusagi, T. Kamachi, I. Takigawa, K.-I. Shimizu, ACS Catal., 2020, 10, 2260-2297.
[40]	M. K. Nielsen, D. T. Ahneman, O. Riera, A. G. Doyle, J. Am. Chem. Soc., 2018, 140, 5004-5008.
[41]	R. Ding, R. Wang, Y. Ding, W. Yin, Y. Liu, J. Li, J. Liu, Angew. Chem. Int. Ed., 2020, 59, 19175-19183.
[42]	S. Bae, H. Lee, J. Shin, H. S. Kim, Y. Kim, D. H. Kim, J. M. Lee, Chem. Mater., 2022, 34, 7761-7773.
[43]	E. Kim, K. Huang, A. Tomala, S. Matthews, E. Strubell, A. Saunders, A. McCallum, E. Olivetti, Sci. Data, 2017, 4, 170127.
[44]	A. Nandy, C. Duan, H. J. Kulik, J. Am. Chem. Soc., 2021, 143, 17535-17547.
[45]	Z. Jensen, E. Kim, S. Kwon, T. Z. H. Gani, Y. Román-Leshkov, M. Moliner, A. Corma, E. Olivetti, ACS Cent. Sci., 2019, 5, 892-899.
[46]	Z. Jensen, S. Kwon, D. Schwalbe-Koda, C. Paris, R. Gómez-Bombarelli, Y. Román-Leshkov, A. Corma, M. Moliner, E. A. Olivetti, ACS Cent. Sci., 2021, 7, 858-867.
[47]	E. Pan, S. Kwon, Z. Jensen, M. Xie, R. Gómez-Bombarelli, M. Moliner, Y. Román-Leshkov, E. Olivetti, ACS Cent. Sci., 2024, 10, 729-743.
[48]	Z. Hajjar, A. Khodadadi, Y. Mortazavi, S. Tayyebi, S. Soltanali, Fuel, 2016, 179, 79-86.
[49]	www.iza-structure.org/databases.
[50]	Z.-M. Cui, Q. Liu, W. Song, L. Wan, Angew. Chem. Int. Ed., 2006, 45, 6512-6515.
[51]	X. Sun, S. Mueller, Y. Liu, H. Shi, G. L. Haller, M. Sanchez-Sanchez, A. C. van Veen, J. A. Lercher, J. Catal., 2014, 317, 185-197.
[52]	G. Yang, Y. Wei, S. Xu, J. Chen, J. Li, Z. Liu, J. Yu, R. Xu, J. Phys. Chem. C, 2013, 117, 8214-8222.
[53]	M. Yang, P. Tian, C. Wang, Y. Yuan, Y. Yang, S. Xu, Y. He, Z. Liu, Chem. Commun., 2014, 50, 1845.
[54]	Z. Li, M. T. Navarro, J. Martínez-Triguero, J. Yu, A. Corma, Catal. Sci. Technol., 2016, 6, 5856-5863.
[55]	P. Ferri, C. Li, R. Millán, J. Martínez-Triguero, M. Moliner, M. Boronat, A. Corma, Angew. Chem. Int. Ed., 2020, 59, 19708-19715.
[56]	W. Zhang, S. Lin, Y. Wei, P. Tian, M. Ye, Z. Liu, Natl. Sci. Rev., 2023, 10, nwad120.
[57]	C. Yuan, Y. Wei, J. Li, S. Xu, J. Chen, Y. Zhou, Q. Wang, L. Xu, Z. Liu, Chin. J. Catal., 2012, 33, 367-374.
[58]	E. M. Gallego, M. T. Portilla, C. Paris, A. León-Escamilla, M. Boronat, M. Moliner, A. Corma, Science, 2017, 355, 1051-1054.
[59]	D. Chen, K. Moljord, T. Fuglerud, A. Holmen, Microporous Mesoporous Mater., 1999, 29, 191-203.
[60]	D. Lesthaeghe, B. De Sterck, V. Van Speybroeck, G. Marin, M. Waroquier, Angew. Chem. Int. Ed., 2007, 46, 1311-1314.
[61]	J. Chen, J. Li, Y. Wei, C. Yuan, B. Li, S. Xu, Y. Zhou, J. Wang, M. Zhang, Z. Liu, Catal. Commun., 2014, 46, 36-40.
[62]	X. Li, Q. Sun, Y. Li, N. Wang, J. Lu, J. Yu, J. Phys. Chem. C, 2014, 118, 24935-24940.
[63]	G. E. P. Box, D. R. Cox, J. R. Stat. Soc. B, 1964, 26, 211-252.
[64]	J. Chen, J. M. Thomas, P. A. Wright, R. P. Townsend, Catal. Lett., 1994, 28, 241-248.
[65]	M. Dusselier, M. A. Deimund, J. E. Schmidt, M. E. Davis, ACS Catal., 2015, 5, 6078-6085.
[66]	N. Venkatathri, Indian J. Chem. A, 2002, 41, 2223-2230.
[67]	W. Yan, J. Yu, R. Xu, G. Zhu, F. Xiao, Y. Han, K. Sugiyama, O. Terasaki, Chem. Mater., 2000, 12, 2517-2519.
[68]	N. K. McGuire, C. A. Bateman, C. Scott Blackwell, S. T. Wilson, R. M. Kirchner, Zeolites, 1995, 15, 460-469.
[69]	D. Xie, Ind. Eng. Chem. Res., 2021, 60, 15403-15415.
[70]	P. Tian, L. Xu, Z. M. Liu, C. L. Sun, T. Huang, Chem. J. Chin. Univ., 2001, 22, 991-994.
[71]	D. Wang, M. Yang, W. Zhang, D. Fan, P. Tian, Z. Liu, CrystEngComm, 2016, 18, 1000-1008.
[72]	R. F. Lobo, S. I. Zones, R. C. Medrud, Chem. Mater., 1996, 8, 2409-2411.
[73]	M. Briend, R. Vomscheid, M. J. Peltre, P. P. Man, D. Barthomeuf, J. Phys. Chem., 1995, 99, 8270-8276.
[74]	S. I. Zones, US 4,544,538, 1985.
[75]	M.-J. Díaz-Cabañas, P. A. Barrett, Chem. Commun., 1998, 1881-1882.
[76]	L. Wang, D. Zhu, J. Wang, W. Cui, J. Han, B. Li, D. Fan, P. Tian, Z. Liu, J. Mater. Chem. A, 2021, 9, 15238-15245.
[77]	I. Yarulina, A. Dikhtiarenko, F. Kapteijn, J. Gascon, Catal. Sci. Technol., 2017, 7, 300-309.
[78]	I. Yarulina, J. Goetze, C. Gücüyener, L. van Thiel, A. Dikhtiarenko, J. Ruiz-Martinez, B. M. Weckhuysen, J. Gascon, F. Kapteijn, Catal. Sci. Technol., 2016, 6, 2663-2678.
[79]	D. Xie, L. B. McCusker, C. Baerlocher, S. I. Zones, W. Wan, X. Zou, J. Am. Chem. Soc., 2013, 135, 10519-10524.
[80]	S. Lin, H. Li, P. Tian, Y. Wei, M. Ye, Z. Liu, J. Am. Chem. Soc., 2025, 147, 11585-11607.
[81]	X. Wang, Y. Zuo, Z. Zhang, C. Wang, M. Yang, P. Tian, Z. Liu, software copyright 2024SR0082804, 2024.
[82]	X. Zhu, Y. Gao, M. Liu, Z. Yang, S. Li, H. Chen, B. Liu, W. Ma, E. J. M. Hensen, B. Shen, Microporous Mesoporous Mater., 2022, 345, 112260.
[83]	E. Borodina, F. Meirer, I. Lezcano-González, M. Mokhtar, A. M. Asiri, S. A. Al-Thabaiti, S. N. Basahel, J. Ruiz-Martinez, B. M. Weckhuysen, ACS Catal., 2015, 5, 992-1003.
[84]	X. Zhu, J. P. Hofmann, B. Mezari, N. Kosinov, L. Wu, Q. Qian, B. M. Weckhuysen, S. Asahina, J. Ruiz-Martínez, E. J. M. Hensen, ACS Catal., 2016, 6, 2163-2177.
[85]	Z. Xu, H. Ma, Y. Huang, W. Qian, H. Zhang, W. Ying, ACS Omega, 2020, 5, 24574-24583.
[86]	F. H. Alshafei, Y. Park, S. I. Zones, M. E. Davis, J. Catal., 2021, 404, 620-633.
[87]	J. Zhou, Y. Zhi, J. Zhang, Z. Liu, T. Zhang, Y. He, A. Zheng, M. Ye, Y. Wei, Z. Liu,, J. Catal., 2019, 377, 153-162.
[88]	C. Zhang, X. Wu, Y. Zhang, W. Zhang, S. Lin, C. Lou, S. Xu, D. He, L. Wang, Y. Wei, Z. Liu, Chem. Catal., 2024, 4, 101025.
[89]	W. Huang, H. Wu, X. Yan, J. Zhang, Z. Feng, M. Yang, P. Tian, X. Guo, L. Ren, Ind. Eng. Chem. Res., 2024, 63, 15438-15447.

机器学习辅助发现具有超高初始选择性的甲醇制烯烃分子筛催化剂

Machine-learning-aided discovery of methanol-to-olefins zeolite catalysts with ultra-high initial selectivity

RichHTML

PDF

Supporting Information

可视化

摘要/Abstract

引用本文

使用本文

扫码分享

图/表 9

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李芳, 张鹏鹤, 王怡然, 刘月明, 何鸣元. FER分子筛酸中心的选择性钝化以识别戊烯单分子催化裂解的位置[J]. 催化学报, 2026, 81(2): 136-147.
[2]	宋鑫, 李中华, 盛利, 刘扬. 单原子催化剂中自旋密度对称性破缺介导的析氢反应[J]. 催化学报, 2026, 80(1): 213-226.
[3]	衣启松, 林露, 耿华伟, 陈少华, 邵元超, 何平, 刘志峰, 许海梅, 陈铁红, 刘远帅, Valentin Valtchev. 氟离子介导酸性体系合成H-ZSM-5分子筛及其液相环己醇催化转化性能研究[J]. 催化学报, 2025, 74(7): 97-107.
[4]	Mansurbek Urol ugli Abdullaev, Woosong Jeon, Yun Kang, Juhwan Noh, Jung Ho Shin, Hee-Joon Chun, Hyun Woo Kim, Yong Tae Kim. 基于机器学习和优化算法的数据驱动框架预测用于合成气制烯烃的氧化物-沸石基复合催化体系和反应条件[J]. 催化学报, 2025, 74(7): 211-227.
[5]	梁婉滢, 许光月, 傅尧. 分子筛限域铜基催化剂上糠醛的精确受限过渡态构型选择性加氢[J]. 催化学报, 2025, 74(7): 71-81.
[6]	庹杰, 盛振腾, 龚贤晨, 杨琪, 吴鹏, 徐浩. ZnCeZrO_x/MCM-22择形催化二氧化碳加氢耦合甲苯甲基化反应[J]. 催化学报, 2025, 73(6): 174-185.
[7]	胡文德, 柯俊, 王仰东, 王传明. 合成气制烯烃双功能催化剂中分子筛拉动效应的微观动力学模拟[J]. 催化学报, 2025, 73(6): 222-233.
[8]	李诚, 房旭东, 李斌, 闫思杨, 陈之旸, 杨磊磊, 郝邵雯, 刘红超, 刘家旭, 朱文良. 酸性分子筛上CO₂与丙烷制芳烃中碳原子高效利用[J]. 催化学报, 2025, 72(5): 314-322.
[9]	郭丽雯, 石导, 章天骏, 马延航, 齐国栋, 徐君, 孙启明. 分子筛硅醇巢锚定的配位不饱和Co位点高效催化丙烷脱氢[J]. 催化学报, 2025, 72(5): 323-333.
[10]	张亚楠, 张雯娜, 张成伟, 何林海, 林杉帆, 徐舒涛, 魏迎旭, 刘中民. C1物种在甲醇制烃类反应过程中的作用: 超越反应物角色[J]. 催化学报, 2025, 71(4): 169-178.
[11]	马千里, 聂红, 杨平, 刘建强, 高鸿毅, 王薇, 董松涛. 基于机器学习的加氢裂化分子筛催化材料构效关系研究[J]. 催化学报, 2025, 71(4): 187-196.
[12]	焦自浩, 张成翼, 刘亚, 郭烈锦, 王子运. 基于图神经网络的高性能二氧化碳还原高熵合金催化剂预测[J]. 催化学报, 2025, 71(4): 197-207.
[13]	刘萌, 苗彩霞, 佛雨萌, 王文璇, 宁尧, 储胜启, 宋卫余, 张瑛, 刘健, 吴志杰, 罗文豪. Ni单原子在BEA沸石中无螯合剂的原位封装提升生物质加氢性能[J]. 催化学报, 2025, 71(4): 308-318.
[14]	刘青, 尚进, 刘振东. 塑料废弃物升级回收时代的分子筛催化剂: 迈向循环经济与可持续发展[J]. 催化学报, 2025, 71(4): 54-69.
[15]	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(3): 311-321.