{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T06:47:15Z","timestamp":1794293235947,"version":"4.9.0"},"reference-count":116,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":104,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A3B1078163"],"award-info":[{"award-number":["2022R1A3B1078163"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Eng"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Heterogeneous catalysis optimization relies on the coordinated selection of catalyst composition, synthesis procedure, and reaction conditions, yet the empirical links between these variables and catalytic performance remain scattered across individual studies. Here, we show that an interpretable machine learning (ML) framework can decode this complexity by integrating disparate literature data. We curate, to our knowledge, the first unified dataset from 200 papers spanning three decades of CO\n                    <jats:sub>2<\/jats:sub>\n                    -to-methanol synthesis over Cu\/ZnO-based catalysts, and derive a subset of apparent activation energy (\n                    <jats:italic>E<\/jats:italic>\n                    <jats:sub>app<\/jats:sub>\n                    ). Using the dataset, we optimize six ML models via Bayesian hyperparameter tuning and apply regularization strategies to mitigate overfitting. We then use SHAP to quantify the non-linear impact of catalyst properties, synthesis procedures, and reaction conditions on methanol selectivity, CO\n                    <jats:sub>2<\/jats:sub>\n                    conversion, and methanol space-time yield. Crucially, this data-driven approach uncovers previously obscured operating windows for achieving high methanol productivity at milder pressures than conventionally assumed. Extending the ML framework to the\n                    <jats:italic>E<\/jats:italic>\n                    <jats:sub>app<\/jats:sub>\n                    kinetic subset further identifies regime-sensitive signatures. A controlled experiment over commercial catalysts corroborates the positive space velocity\u2013\n                    <jats:italic>E<\/jats:italic>\n                    <jats:sub>app<\/jats:sub>\n                    relationship inferred from ML models. These results show how interpretable ML built on curated legacy experiments can support operating-window selection, hypothesis-driven kinetic experiments, and practical optimization of mature catalytic technologies.\n                  <\/jats:p>","DOI":"10.1038\/s44172-026-00706-4","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:45:23Z","timestamp":1781048723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4465-7342","authenticated-orcid":false,"given":"Mo","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengxi","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Asif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuning Anugrah Putri","family":"Namari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joohwan","family":"Ha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaewon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dayoung","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seokchan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungjae","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8723-8286","authenticated-orcid":false,"given":"Xiaoming","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-7668","authenticated-orcid":false,"given":"Taesung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"706_CR1","doi-asserted-by":"publisher","first-page":"15648","DOI":"10.1021\/jacs.4c01305","volume":"146","author":"SA Bonke","year":"2024","unstructured":"Bonke, S. A. et al. Multi-variable multi-metric optimization of self-assembled photocatalytic CO2 reduction performance using machine learning algorithms. J. Am. Chem. Soc. 146, 15648\u201315658 (2024).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR2","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1038\/s41586-024-07021-y","volume":"626","author":"JY Wang","year":"2024","unstructured":"Wang, J. Y. et al. Identifying general reaction conditions by bandit optimization. Nature 626, 1025\u20131033 (2024).","journal-title":"Nature"},{"key":"706_CR3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-021-03213-y","volume":"590","author":"BJ Shields","year":"2021","unstructured":"Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89\u201396 (2021).","journal-title":"Nature"},{"key":"706_CR4","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1126\/science.adp6034","volume":"386","author":"T Wang","year":"2024","unstructured":"Wang, T. et al. Nature of metal-support interaction for metal catalysts on oxide supports. Science 386, 915\u2013920 (2024).","journal-title":"Science"},{"key":"706_CR5","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/s41586-018-0307-8","volume":"559","author":"JM Granda","year":"2018","unstructured":"Granda, J. M., Donina, L., Dragone, V., Long, D. L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377\u2013381 (2018).","journal-title":"Nature"},{"key":"706_CR6","doi-asserted-by":"publisher","first-page":"9847","DOI":"10.1002\/anie.201705148","volume":"56","author":"CW Coley","year":"2017","unstructured":"Coley, C. W., Abolhasani, M., Lin, H. & Jensen, K. F. Material-efficient microfluidic platform for exploratory studies of visible-light photoredox catalysis. Angew. Chem. Int. Ed. 56, 9847\u20139850 (2017).","journal-title":"Angew. Chem. Int. Ed."},{"key":"706_CR7","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1038\/s41586-020-2083-5","volume":"579","author":"S Chatterjee","year":"2020","unstructured":"Chatterjee, S., Guidi, M., Seeberger, P. H. & Gilmore, K. Automated radial synthesis of organic molecules. Nature 579, 379\u2013384 (2020).","journal-title":"Nature"},{"key":"706_CR8","doi-asserted-by":"publisher","DOI":"10.1126\/science.aao0185","volume":"359","author":"S Fortunato","year":"2018","unstructured":"Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).","journal-title":"Science"},{"key":"706_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-20924-y","volume":"12","author":"AM Limaye","year":"2021","unstructured":"Limaye, A. M., Zeng, J. S., Willard, A. P. & Manthiram, K. Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO2 reduction electrocatalysis. Nat. Commun. 12, 703 (2021).","journal-title":"Nat. Commun."},{"key":"706_CR10","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1038\/s41557-019-0282-1","volume":"11","author":"JM Quan","year":"2019","unstructured":"Quan, J. M. et al. Vibration-driven reaction of CO2 on Cu surfaces via Eley\u2013Rideal-type mechanism. Nat. Chem. 11, 722\u2013729 (2019).","journal-title":"Nat. Chem."},{"key":"706_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2023.130852","volume":"363","author":"Q Jiang","year":"2024","unstructured":"Jiang, Q. et al. Enhanced oxygen migration in tailored lanthanum-based perovskite for solar-driven dry reforming of methane. Fuel 363, 130852 (2024).","journal-title":"Fuel"},{"key":"706_CR12","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1039\/D3EY00246B","volume":"2","author":"Y Haghshenas","year":"2024","unstructured":"Haghshenas, Y. et al. Predicting the rates of photocatalytic hydrogen evolution over cocatalyst-deposited TiO2 using machine learning with active photon flux as a unifying feature. EES Catal. 2, 612\u2013623 (2024).","journal-title":"EES Catal."},{"key":"706_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-58854-8","volume":"16","author":"S Singh","year":"2025","unstructured":"Singh, S. & Hern\u00e1ndez-Lobato, J. M. A meta-learning approach for selectivity prediction in asymmetric catalysis. Nat. Commun. 16, 3599 (2025).","journal-title":"Nat. Commun."},{"key":"706_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/anie.202318487","volume":"63","author":"P Baczewska","year":"2024","unstructured":"Baczewska, P. et al. Machine learning algorithm guides catalyst choices for magnesium-catalyzed asymmetric reactions. Angew. Chem. Int. Ed. 63, e202318487 (2024).","journal-title":"Angew. Chem. Int. Ed."},{"key":"706_CR15","first-page":"101111","volume":"4","author":"S Chen","year":"2024","unstructured":"Chen, S. & Pollice, R. Predicting hydroformylation regioselectivity from literature data via machine learning. Chem. Catal. 4, 101111 (2024).","journal-title":"Chem. Catal."},{"key":"706_CR16","doi-asserted-by":"publisher","first-page":"13266","DOI":"10.1021\/jacs.4c01352","volume":"146","author":"L Schlosser","year":"2024","unstructured":"Schlosser, L., Rana, D., Pfl\u00fcger, P., Katzenburg, F. & Glorius, F. EnTdecker: a machine learning-based platform for guiding substrate discovery in energy transfer catalysis. J. Am. Chem. Soc. 146, 13266\u201313275 (2024).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR17","doi-asserted-by":"publisher","first-page":"122521","DOI":"10.1016\/j.watres.2024.122521","volume":"267","author":"W Zhuang","year":"2024","unstructured":"Zhuang, W. et al. Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process. Water Res. 267, 122521 (2024).","journal-title":"Water Res"},{"key":"706_CR18","doi-asserted-by":"publisher","first-page":"31074","DOI":"10.1039\/D4TA04991H","volume":"12","author":"AP Dmitrieva","year":"2024","unstructured":"Dmitrieva, A. P. et al. AI and ML for selecting viable electrocatalysts: progress and perspectives. J. Mater. Chem. A 12, 31074\u201331102 (2024).","journal-title":"J. Mater. Chem. A"},{"key":"706_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/anie.202502410","volume":"64","author":"Y Song","year":"2025","unstructured":"Song, Y., Ding, Y., Su, J., Li, J. & Ji, Y. Unlocking the potential of machine learning in co-crystal prediction by a novel approach integrating molecular thermodynamics. Angew. Chem. Int. Ed. 64, e202502410 (2025).","journal-title":"Angew. Chem. Int. Ed."},{"key":"706_CR20","doi-asserted-by":"publisher","first-page":"e2420465","DOI":"10.1002\/adma.202420465","volume":"37","author":"M Suvarna","year":"2025","unstructured":"Suvarna, M., Usteri, M. E., Krumeich, F., Mitchell, S. & P\u00e9rez-Ram\u00edrez, J. Explainable artificial intelligence elucidates synthesis-structure-property-function relationships in nanostructured catalysts. Adv. Mater. 37, e2420465 (2025).","journal-title":"Adv. Mater."},{"key":"706_CR21","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1021\/acscentsci.3c01615","volume":"10","author":"E Pan","year":"2024","unstructured":"Pan, E. et al. ZeoSyn: a comprehensive zeolite synthesis dataset enabling machine-learning rationalization of hydrothermal parameters. ACS Cent. Sci. 10, 729\u2013743 (2024).","journal-title":"ACS Cent. Sci."},{"key":"706_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/aic.18437","volume":"70","author":"Q Yang","year":"2024","unstructured":"Yang, Q., Fan, Y., Rong, D., Bao, R. & Zhang, D. An auto-configurable machine learning framework to optimize and predict catalysts for CO2 to light olefins process. AIChE J. 70, e18437 (2024).","journal-title":"AIChE J."},{"key":"706_CR23","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s42004-023-01086-y","volume":"7","author":"T Taniike","year":"2024","unstructured":"Taniike, T., Fujiwara, A., Nakanowatari, S., Garc\u00eda-Escobar, F. & Takahashi, K. Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis. Commun. Chem. 7, 11 (2024).","journal-title":"Commun. Chem."},{"key":"706_CR24","doi-asserted-by":"publisher","first-page":"18","DOI":"10.20517\/jmi.2023.23","volume":"3","author":"Y Hu","year":"2023","unstructured":"Hu, Y., Chen, J., Wei, Z., He, Q. & Zhao, Y. Recent advances and applications of machine learning in electrocatalysis. J. Mater. Inf. 3, 18 (2023).","journal-title":"J. Mater. Inf."},{"key":"706_CR25","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41929-018-0142-1","volume":"1","author":"K Tran","year":"2018","unstructured":"Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696\u2013703 (2018).","journal-title":"Nat. Catal."},{"key":"706_CR26","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1063\/1.4812323","volume":"1","author":"A Jain","year":"2013","unstructured":"Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).","journal-title":"APL Mater."},{"key":"706_CR27","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s11837-013-0755-4","volume":"65","author":"JE Saal","year":"2013","unstructured":"Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65, 1501\u20131509 (2013).","journal-title":"JOM"},{"key":"706_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43118-0","volume":"14","author":"DH Mok","year":"2023","unstructured":"Mok, D. H. et al. Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning. Nat. Commun. 14, 7303 (2023).","journal-title":"Nat. Commun."},{"key":"706_CR29","doi-asserted-by":"publisher","first-page":"18106","DOI":"10.1039\/D3TA03115B","volume":"11","author":"X He","year":"2023","unstructured":"He, X. et al. Uncovering the influence of the modifier redox potential on CO2 reduction through combined data-driven machine learning and hypothesis-driven experimentation. J. Mater. Chem. A 11, 18106\u201318114 (2023).","journal-title":"J. Mater. Chem. A"},{"key":"706_CR30","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1021\/acs.jpcc.2c08343","volume":"127","author":"E Hu","year":"2023","unstructured":"Hu, E., Liu, C., Zhang, W. & Yan, Q. Machine learning-assisted understanding and discovery of CO2 reduction reaction electrocatalyst. J. Phys. Chem. C. 127, 882\u2013893 (2023).","journal-title":"J. Phys. Chem. C."},{"key":"706_CR31","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1038\/s41570-021-00289-y","volume":"5","author":"S Navarro-Ja\u00e9n","year":"2021","unstructured":"Navarro-Ja\u00e9n, S. et al. Highlights and challenges in the selective reduction of carbon dioxide to methanol. Nat. Rev. Chem. 5, 564\u2013579 (2021).","journal-title":"Nat. Rev. Chem."},{"key":"706_CR32","doi-asserted-by":"publisher","DOI":"10.1126\/science.adn9388","volume":"387","author":"J Ye","year":"2025","unstructured":"Ye, J. et al. Hydrogenation of CO2 for sustainable fuel and chemical production. Science 387, eadn9388 (2025).","journal-title":"Science"},{"key":"706_CR33","doi-asserted-by":"publisher","first-page":"56151","DOI":"10.1021\/acsami.1c16696","volume":"13","author":"D Roy","year":"2021","unstructured":"Roy, D., Mandal, S. C. & Pathak, B. Machine learning-driven high-throughput screening of alloy-based catalysts for selective CO2 hydrogenation to methanol. ACS Appl. Mater. Interfaces 13, 56151\u201356163 (2021).","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"706_CR34","doi-asserted-by":"publisher","first-page":"121530","DOI":"10.1016\/j.apcatb.2022.121530","volume":"315","author":"M Suvarna","year":"2022","unstructured":"Suvarna, M., Ara\u00fajo, T. P. & P\u00e9rez-Ram\u00edrez, J. A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation. Appl. Catal. B 315, 121530 (2022).","journal-title":"Appl. Catal. B"},{"key":"706_CR35","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.matpr.2022.11.265","volume":"72","author":"P Vanjari","year":"2023","unstructured":"Vanjari, P., Kamesh, R. & Rani, K. Y. Machine learning models representing catalytic activity for direct catalytic CO2 hydrogenation to methanol. Mater. Today Proc. 72, 524\u2013532 (2023).","journal-title":"Mater. Today Proc."},{"key":"706_CR36","doi-asserted-by":"publisher","first-page":"119373","DOI":"10.1016\/j.enconman.2024.119373","volume":"325","author":"SA Ghafarian Nia","year":"2025","unstructured":"Ghafarian Nia, S. A. et al. Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation. Energy Convers. Manag. 325, 119373 (2025).","journal-title":"Energy Convers. Manag."},{"key":"706_CR37","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1116\/1.579970","volume":"14","author":"I Nakamura","year":"1996","unstructured":"Nakamura, I., Fujitani, T., Uchijima, T. & Nakamura, J. A model catalyst for methanol synthesis: Zn-deposited and Zn-free Cu surfaces. J. Vac. Sci. Technol. A 14, 1464\u20131468 (1996).","journal-title":"J. Vac. Sci. Technol. A"},{"key":"706_CR38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0926-860X(99)00313-0","volume":"191","author":"T Fujitani","year":"2000","unstructured":"Fujitani, T. & Nakamura, J. The chemical modification seen in the Cu\/ZnO methanol synthesis catalysts. Appl. Catal. A Gen. 191, 111\u2013129 (2000).","journal-title":"Appl. Catal. A Gen."},{"key":"706_CR39","doi-asserted-by":"publisher","first-page":"2409322","DOI":"10.1002\/adma.202409322","volume":"36","author":"TP Ara\u00fajo","year":"2024","unstructured":"Ara\u00fajo, T. P., Mitchell, S. & P\u00e9rez-Ram\u00edrez, J. Design principles of catalytic materials for CO2 hydrogenation to methanol. Adv. Mater. 36, 2409322 (2024).","journal-title":"Adv. Mater."},{"key":"706_CR40","doi-asserted-by":"publisher","first-page":"13401","DOI":"10.1021\/jacs.2c06044","volume":"144","author":"YF Shi","year":"2022","unstructured":"Shi, Y. F., Kang, P. L., Shang, C. & Liu, Z. P. Methanol synthesis from CO2\/CO mixture on Cu-Zn catalysts from microkinetics-guided machine learning pathway search. J. Am. Chem. Soc. 144, 13401\u201313414 (2022).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR41","unstructured":"Rohatgi, A. WebPlotDigitizer: extract data from plots, images, and maps. https:\/\/automeris.io\/WebPlotDigitizer\/ (2010)."},{"key":"706_CR42","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","volume":"53","author":"WC Lin","year":"2020","unstructured":"Lin, W. C. & Tsai, C. F. Missing value imputation: a review and analysis of the literature (2006\u20132017). Artif. Intell. Rev. 53, 1487\u20131509 (2020).","journal-title":"Artif. Intell. Rev."},{"key":"706_CR43","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1016\/j.procs.2019.11.177","volume":"161","author":"F Ridzuan","year":"2019","unstructured":"Ridzuan, F. & Wan Zainon, W. M. N. A review on data cleansing methods for big data. Procedia Comput. Sci. 161, 731\u2013738 (2019).","journal-title":"Procedia Comput. Sci."},{"key":"706_CR44","doi-asserted-by":"publisher","first-page":"16028","DOI":"10.1038\/npjcompumats.2016.28","volume":"2","author":"L Ward","year":"2016","unstructured":"Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. A general-purpose machine learning framework for predicting properties of inorganic materials. NPJ Comput. Mater. 2, 16028 (2016).","journal-title":"NPJ Comput. Mater."},{"key":"706_CR45","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/S1525-1578(10)60455-2","volume":"5","author":"C Cheadle","year":"2003","unstructured":"Cheadle, C., Vawter, M. P., Freed, W. J. & Becker, K. G. Analysis of microarray data using Z score transformation. J. Mol. Diagn. 5, 73\u201381 (2003).","journal-title":"J. Mol. Diagn."},{"key":"706_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.apcatb.2023.123454","volume":"343","author":"J Roh","year":"2024","unstructured":"Roh, J. et al. Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane. Appl. Catal. B 343, 123454 (2024).","journal-title":"Appl. Catal. B"},{"key":"706_CR47","doi-asserted-by":"publisher","first-page":"5755","DOI":"10.1021\/jacs.1c00339","volume":"143","author":"Y Guo","year":"2021","unstructured":"Guo, Y. et al. Machine-learning-guided discovery and optimization of additives in preparing Cu catalysts for CO2 reduction. J. Am. Chem. Soc. 143, 5755\u20135762 (2021).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR48","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/S0166-218X(01)00362-6","volume":"121","author":"A Vince","year":"2002","unstructured":"Vince, A. A framework for the greedy algorithm. Discret. Appl. Math. 121, 247\u2013260 (2002).","journal-title":"Discret. Appl. Math."},{"key":"706_CR49","doi-asserted-by":"publisher","first-page":"2500311","DOI":"10.1002\/aisy.202500311","volume":"7","author":"C Yao","year":"2025","unstructured":"Yao, C. et al. Exploring the influence of applied potential and catalyst morphology on the selectivities of copper toward carbon dioxide electroreduction using machine learning. Adv. Intell. Syst. 7, 2500311 (2025).","journal-title":"Adv. Intell. Syst."},{"key":"706_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19594-z","volume":"11","author":"S Boobier","year":"2020","unstructured":"Boobier, S., Hose, D. R. J., Blacker, A. J. & Nguyen, B. N. Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. Nat. Commun. 11, 5753 (2020).","journal-title":"Nat. Commun."},{"key":"706_CR51","first-page":"117","volume":"153","author":"D Rotman","year":"1993","unstructured":"Rotman, D. & Chynoweth, E. The quest for reduced emissions, greener processes. Chem. Week 153, 117 (1993).","journal-title":"Chem. Week"},{"key":"706_CR52","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1023\/A:1019082525994","volume":"2","author":"M Saito","year":"1998","unstructured":"Saito, M. R&D activities in Japan on methanol synthesis from CO2 and H2. Catal. Surv. Jpn. 2, 175\u2013184 (1998).","journal-title":"Catal. Surv. Jpn."},{"key":"706_CR53","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1006\/jcat.1995.1306","volume":"157","author":"S Fujita","year":"1995","unstructured":"Fujita, S., Usui, M., Ito, H. & Takezawa, N. Mechanisms of methanol synthesis from carbon dioxide and from carbon monoxide at atmospheric pressure over Cu\/ZnO. J. Catal. 157, 403\u2013413 (1995).","journal-title":"J. Catal."},{"key":"706_CR54","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/S0169-4332(97)00372-3","volume":"121","author":"T Fujitani","year":"1997","unstructured":"Fujitani, T., Nakamura, I., Ueno, S., Uchijima, T. & Nakamura, J. Methanol synthesis by hydrogenation of CO2 over a Zn-deposited Cu(111): formate intermediate. Appl. Surf. Sci. 121, 583\u2013586 (1997).","journal-title":"Appl. Surf. Sci."},{"key":"706_CR55","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1023\/A:1019025816831","volume":"58","author":"T Fujitani","year":"1999","unstructured":"Fujitani, T., Nakamura, J. & Waugh, K. C. Comments on \u2018The effect of ZnO in methanol synthesis catalysts on Cu dispersion and the specific activity. Catal. Lett. 58, 163\u2013165 (1999).","journal-title":"Catal. Lett."},{"key":"706_CR56","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/S0926-860X(00)00712-2","volume":"208","author":"Y Choi","year":"2001","unstructured":"Choi, Y., Futagami, K., Fujitani, T. & Nakamura, J. Role of ZnO in Cu\/ZnO methanol synthesis catalysts: morphology effect or active site model? Appl. Catal. A Gen. 208, 163\u2013167 (2001).","journal-title":"Appl. Catal. A Gen."},{"key":"706_CR57","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cattod.2012.07.029","volume":"197","author":"A Karelovic","year":"2012","unstructured":"Karelovic, A., Bargibant, A., Fern\u00e1ndez, C. & Ruiz, P. Effect of the structural and morphological properties of Cu\/ZnO catalysts prepared by citrate method on their activity toward methanol synthesis from CO2 and H2 under mild reaction conditions. Catal. Today 197, 109\u2013118 (2012).","journal-title":"Catal. Today"},{"key":"706_CR58","doi-asserted-by":"publisher","first-page":"604","DOI":"10.9767\/bcrec.14.3.3451.604-613","volume":"14","author":"D Allam","year":"2019","unstructured":"Allam, D., Cheknoun, S. & Hocine, S. Operating conditions and composition effect on the hydrogenation of carbon dioxide performed over CuO\/ZnO\/Al2O3 catalysts. Bull. Chem. React. Eng. Catal. 14, 604\u2013613 (2019).","journal-title":"Bull. Chem. React. Eng. Catal."},{"key":"706_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.apcatb.2020.119382","volume":"279","author":"F Chen","year":"2020","unstructured":"Chen, F. et al. Vapor-phase low-temperature methanol synthesis from CO2-containing syngas via self-catalysis of methanol and Cu\/ZnO catalysts prepared by solid-state method. Appl. Catal. B 279, 119382 (2020).","journal-title":"Appl. Catal. B"},{"key":"706_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2022.123272","volume":"315","author":"F Chen","year":"2022","unstructured":"Chen, F. et al. Enhanced performance and stability of Cu\/ZnO catalyst by introducing MgO for low-temperature methanol synthesis using methanol itself as catalytic promoter. Fuel 315, 123272 (2022).","journal-title":"Fuel"},{"key":"706_CR61","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21604-7","volume":"12","author":"NJ Divins","year":"2021","unstructured":"Divins, N. J. et al. Operando high-pressure investigation of size-controlled CuZn catalysts for the methanol synthesis reaction. Nat. Commun. 12, 1435 (2021).","journal-title":"Nat. Commun."},{"key":"706_CR62","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.jcou.2017.09.012","volume":"22","author":"J D\u00edez-Ram\u00edrez","year":"2017","unstructured":"D\u00edez-Ram\u00edrez, J., D\u00edaz, J. A., S\u00e1nchez, P. & Dorado, F. Optimization of the Pd\/Cu ratio in Pd-Cu-Zn\/SiC catalysts for the CO2 hydrogenation to methanol at atmospheric pressure. J. CO2 Util. 22, 71\u201380 (2017).","journal-title":"J. CO2 Util."},{"key":"706_CR63","first-page":"38","volume":"456","author":"M Sadeghinia","year":"2018","unstructured":"Sadeghinia, M., Ghaziani, A. N. K. & Rezaei, M. Component-ratio-dependent Cu\/Zn\/Al structure-sensitive catalyst in CO2\/CO hydrogenation to methanol. Mol. Catal. 456, 38\u201348 (2018).","journal-title":"Mol. Catal."},{"key":"706_CR64","doi-asserted-by":"publisher","first-page":"207","DOI":"10.17576\/jsm-2018-4701-24","volume":"47","author":"S Shaharun","year":"2018","unstructured":"Shaharun, S., Shaharun, M. S., Shah, M. F. M. & Amer, N. A. Catalytic performance of Cu\/ZnO\/Al2O3\/ZrO2 for slurry methanol synthesis from CO hydrogenation: effect of Cu\/Zn molar ratio. Sains Malays. 47, 207\u2013214 (2018).","journal-title":"Sains Malays."},{"key":"706_CR65","doi-asserted-by":"publisher","first-page":"101215","DOI":"10.1016\/j.jcou.2020.101215","volume":"41","author":"FCF Marcos","year":"2020","unstructured":"Marcos, F. C. F. et al. Insights into the methanol synthesis mechanism via CO2 hydrogenation over Cu-ZnO-ZrO2 catalysts: effects of surfactant\/Cu-Zn-Zr molar ratio. J. CO2 Util. 41, 101215 (2020).","journal-title":"J. CO2 Util."},{"key":"706_CR66","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/S0039-6028(97)00192-1","volume":"383","author":"T Fujitani","year":"1997","unstructured":"Fujitani, T., Nakamura, I., Uchijima, T. & Nakamura, J. The kinetics and mechanism of methanol synthesis by hydrogenation of CO2 over a Zn-deposited Cu(111) surface. Surf. Sci. 383, 285\u2013298 (1997).","journal-title":"Surf. Sci."},{"key":"706_CR67","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/S0039-6028(97)00899-6","volume":"400","author":"I Nakamura","year":"1998","unstructured":"Nakamura, I., Fujitani, T., Uchijima, T. & Nakamura, J. The synthesis of methanol and the reverse water-gas shift reaction over Zn-deposited Cu(100) and Cu(110) surfaces: comparison with Zn\/Cu(111). Surf. Sci. 400, 387\u2013400 (1998).","journal-title":"Surf. Sci."},{"key":"706_CR68","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.cattod.2011.03.073","volume":"172","author":"RT Figueiredo","year":"2011","unstructured":"Figueiredo, R. T., Santos, M. S., Andrade, H. M. C. & Fierro, J. L. G. Effect of alkali cations on the CuZnOAl2O3 low temperature water gas-shift catalyst. Catal. Today 172, 166\u2013170 (2011).","journal-title":"Catal. Today"},{"key":"706_CR69","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.cattod.2015.09.044","volume":"263","author":"CL Wang","year":"2016","unstructured":"Wang, C. L. et al. The water-gas shift reaction for hydrogen production from coke oven gas over Cu\/ZnO\/Al2O3 catalyst. Catal. Today 263, 46\u201351 (2016).","journal-title":"Catal. Today"},{"key":"706_CR70","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1116\/1.577930","volume":"10","author":"I Chorkendorff","year":"1992","unstructured":"Chorkendorff, I., Taylor, P. A. & Rasmussen, P. B. Synthesis and hydrogenation of formate on Cu(100) at high pressures. J. Vac. Sci. Technol. A 10, 2277\u20132281 (1992).","journal-title":"J. Vac. Sci. Technol. A"},{"key":"706_CR71","doi-asserted-by":"publisher","first-page":"106373","DOI":"10.1016\/j.catcom.2021.106373","volume":"162","author":"YN Yang","year":"2022","unstructured":"Yang, Y. N., Huang, C. W., Nguyen, V. H. & Wu, J. C. S. Enhanced methanol production by two-stage reaction of CO2 hydrogenation at atmospheric pressure. Catal. Commun. 162, 106373 (2022).","journal-title":"Catal. Commun."},{"key":"706_CR72","doi-asserted-by":"publisher","first-page":"21913","DOI":"10.1016\/j.ijhydene.2019.06.176","volume":"44","author":"X Fang","year":"2019","unstructured":"Fang, X. et al. Moderate-pressure conversion of H2 and CO2 to methanol via adsorption enhanced hydrogenation. Int. J. Hydrog. Energy 44, 21913\u201321925 (2019).","journal-title":"Int. J. Hydrog. Energy"},{"key":"706_CR73","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.fuproc.2018.01.024","volume":"173","author":"J D\u00edez-Ram\u00edrez","year":"2018","unstructured":"D\u00edez-Ram\u00edrez, J., D\u00edaz, J. A., Dorado, F. & S\u00e1nchez, P. Kinetics of the hydrogenation of CO2 to methanol at atmospheric pressure using a Pd-Cu-Zn\/SiC catalyst. Fuel Process. Technol. 173, 173\u2013181 (2018).","journal-title":"Fuel Process. Technol."},{"key":"706_CR74","doi-asserted-by":"publisher","first-page":"117804","DOI":"10.1016\/j.fuel.2020.117804","volume":"274","author":"D Previtali","year":"2020","unstructured":"Previtali, D. et al. Low pressure conversion of CO2 to methanol over Cu\/Zn\/Al catalysts: the effect of Mg, Ca and Sr as basic promoters. Fuel 274, 117804 (2020).","journal-title":"Fuel"},{"key":"706_CR75","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jcat.2012.12.019","volume":"300","author":"F Arena","year":"2013","unstructured":"Arena, F. et al. Effects of oxide carriers on surface functionality and process performance of the Cu-ZnO system in the synthesis of methanol via CO2 hydrogenation. J. Catal. 300, 141\u2013151 (2013).","journal-title":"J. Catal."},{"key":"706_CR76","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/BF00808596","volume":"31","author":"J Nakamura","year":"1995","unstructured":"Nakamura, J. et al. Methanol synthesis over a Zn-deposited copper model catalyst. Catal. Lett. 31, 325\u2013331 (1995).","journal-title":"Catal. Lett."},{"key":"706_CR77","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/S0926-860X(00)00871-1","volume":"211","author":"W Ning","year":"2001","unstructured":"Ning, W., Shen, H. & Liu, H. Study of the effect of preparation method on CuO-ZnO-Al2O3 catalyst. Appl. Catal. A Gen. 211, 153\u2013157 (2001).","journal-title":"Appl. Catal. A Gen."},{"key":"706_CR78","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jcou.2016.11.015","volume":"17","author":"C Zhang","year":"2017","unstructured":"Zhang, C. et al. Preparation and CO2 hydrogenation catalytic properties of alumina microsphere supported Cu-based catalyst by deposition-precipitation method. J. CO2 Util. 17, 263\u2013272 (2017).","journal-title":"J. CO2 Util."},{"key":"706_CR79","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cattod.2018.03.046","volume":"317","author":"SA Kondrat","year":"2018","unstructured":"Kondrat, S. A. et al. Preparation of a highly active ternary Cu-Zn-Al oxide methanol synthesis catalyst by supercritical CO2 anti-solvent precipitation. Catal. Today 317, 12\u201320 (2018).","journal-title":"Catal. Today"},{"key":"706_CR80","doi-asserted-by":"publisher","first-page":"15085","DOI":"10.1021\/acs.iecr.2c00391","volume":"61","author":"TTN Vu","year":"2022","unstructured":"Vu, T. T. N. et al. Metallurgical residue-derived Cu-ZnO-based catalyst for CO2 hydrogenation to methanol: an insight on the effect of the preparation method. Ind. Eng. Chem. Res. 61, 15085\u201315102 (2022).","journal-title":"Ind. Eng. Chem. Res."},{"key":"706_CR81","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1126\/science.1219831","volume":"336","author":"M Behrens","year":"2012","unstructured":"Behrens, M. et al. The active site of methanol synthesis over Cu\/ZnO\/Al2O3 industrial catalysts. Science 336, 893\u2013897 (2012).","journal-title":"Science"},{"key":"706_CR82","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.nanoen.2017.11.021","volume":"43","author":"X Zhang","year":"2018","unstructured":"Zhang, X. et al. Optimum Cu nanoparticle catalysts for CO2 hydrogenation towards methanol. Nano Energy 43, 200\u2013209 (2018).","journal-title":"Nano Energy"},{"key":"706_CR83","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1039\/C9CS00614A","volume":"49","author":"J Zhong","year":"2020","unstructured":"Zhong, J. et al. State of the art and perspectives in heterogeneous catalysis of CO2 hydrogenation to methanol. Chem. Soc. Rev. 49, 1385\u20131413 (2020).","journal-title":"Chem. Soc. Rev."},{"key":"706_CR84","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1126\/science.aaf0718","volume":"352","author":"S Kuld","year":"2016","unstructured":"Kuld, S. et al. Quantifying the promotion of Cu catalysts by ZnO for methanol synthesis. Science 352, 969\u2013974 (2016).","journal-title":"Science"},{"key":"706_CR85","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1595\/205651325X17176890228226","volume":"68","author":"M Al Salmi","year":"2024","unstructured":"Al Salmi, M. Active sites of Cu\/ZnO-based catalysts for CO2 hydrogenation to methanol. Johns. Matthey Technol. Rev. 68, 490\u2013502 (2024).","journal-title":"Johns. Matthey Technol. Rev."},{"key":"706_CR86","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1126\/science.aal3573","volume":"355","author":"S Kattel","year":"2017","unstructured":"Kattel, S., Ram\u00edrez, P. J., Chen, J. G., Rodriguez, J. A. & Liu, P. Active sites for CO2 hydrogenation to methanol on Cu\/ZnO catalysts. Science 355, 1296\u20131299 (2017).","journal-title":"Science"},{"key":"706_CR87","doi-asserted-by":"publisher","DOI":"10.1126\/science.aan8074","volume":"357","author":"J Nakamura","year":"2017","unstructured":"Nakamura, J. et al. Comment on \u2018Active sites for CO2 hydrogenation to methanol on Cu\/ZnO catalysts\u2019. Science 357, eaan8074 (2017).","journal-title":"Science"},{"key":"706_CR88","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1006\/jcat.1996.0124","volume":"160","author":"J Nakamura","year":"1996","unstructured":"Nakamura, J. et al. A surface science investigation of methanol synthesis over a Zn-deposited polycrystalline Cu surface. J. Catal. 160, 65\u201375 (1996).","journal-title":"J. Catal."},{"key":"706_CR89","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1023\/A:1023588322846","volume":"22","author":"J Nakamura","year":"2003","unstructured":"Nakamura, J., Choi, Y. & Fujitani, T. On the issue of the active site and the role of ZnO in Cu\/ZnO methanol synthesis catalysts. Top. Catal. 22, 277\u2013285 (2003).","journal-title":"Top. Catal."},{"key":"706_CR90","doi-asserted-by":"publisher","first-page":"12158","DOI":"10.1021\/jacs.2c02797","volume":"144","author":"K Takeyasu","year":"2022","unstructured":"Takeyasu, K. et al. Hydrogenation of formate species using atomic hydrogen on a Cu(111) model catalyst. J. Am. Chem. Soc. 144, 12158\u201312166 (2022).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR91","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1023\/A:1019069708459","volume":"49","author":"T Fujitani","year":"1997","unstructured":"Fujitani, T. et al. Creation of the active site for methanol synthesis on a Cu\/SiO2 catalyst. Catal. Lett. 49, 175\u2013179 (1997).","journal-title":"Catal. Lett."},{"key":"706_CR92","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/BF00806562","volume":"38","author":"Y Kanai","year":"1996","unstructured":"Kanai, Y., Watanabe, T., Fujitani, T., Uchijima, T. & Nakamura, J. The synergy between Cu and ZnO in methanol synthesis catalysts. Catal. Lett. 38, 157\u2013163 (1996).","journal-title":"Catal. Lett."},{"key":"706_CR93","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/0920-5861(95)00240-5","volume":"28","author":"J Nakamura","year":"1996","unstructured":"Nakamura, J., Uchijima, T., Kanai, Y. & Fujitani, T. The role of ZnO in Cu\/ZnO methanol synthesis catalysts. Catal. Today 28, 223\u2013230 (1996).","journal-title":"Catal. Today"},{"key":"706_CR94","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1126\/science.1205438","volume":"334","author":"DN Reshef","year":"2011","unstructured":"Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518\u20131524 (2011).","journal-title":"Science"},{"key":"706_CR95","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1484","volume":"13","author":"B Bischl","year":"2023","unstructured":"Bischl, B. et al. Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 13, e1484 (2023).","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"706_CR96","doi-asserted-by":"crossref","unstructured":"Del Buono, N., Esposito, F. & Selicato, L. Methods for hyperparameters optimization in learning approaches: an overview. In Lecture Notes in Computer Science 12565, 100\u2013112 (Springer, 2020).","DOI":"10.1007\/978-3-030-64583-0_11"},{"key":"706_CR97","doi-asserted-by":"crossref","unstructured":"Alibrahim, H. & Ludwig, S. A. Hyperparameter optimization: comparing genetic algorithm against grid search and Bayesian optimization. In 2021 IEEE Congress on Evolutionary Computation. 1551\u20131559 (2021).","DOI":"10.1109\/CEC45853.2021.9504761"},{"key":"706_CR98","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: a next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2623\u20132631 (Association for Computing Machinery, 2019).","DOI":"10.1145\/3292500.3330701"},{"key":"706_CR99","doi-asserted-by":"crossref","unstructured":"Shapley, L. S. A value for n-person games. In Contributions to the Theory of Games (AM-28), Volume II, 307\u2013318 (Princeton University Press, 2016).","DOI":"10.1515\/9781400881970-018"},{"key":"706_CR100","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41570-025-00740-4","volume":"9","author":"C Bozal-Ginesta","year":"2025","unstructured":"Bozal-Ginesta, C., Pablo-Garc\u00eda, S., Choi, C., Taranc\u00f3n, A. & Aspuru-Guzik, A. Developing machine learning for heterogeneous catalysis with experimental and computational data. Nat. Rev. Chem. 9, 601\u2013616 (2025).","journal-title":"Nat. Rev. Chem."},{"key":"706_CR101","doi-asserted-by":"publisher","first-page":"155826","DOI":"10.1016\/j.cej.2024.155826","volume":"499","author":"Y Shu","year":"2024","unstructured":"Shu, Y. et al. Interface-engineered inverse ZnO\/Cu for low-temperature CO2 hydrogenation to methanol. Chem. Eng. J. 499, 155826 (2024).","journal-title":"Chem. Eng. J."},{"key":"706_CR102","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/BF02699049","volume":"17","author":"JS Lee","year":"2000","unstructured":"Lee, J. S., Han, S. H., Kim, H. G., Lee, K. H. & Kim, Y. G. Effects of space velocity on methanol synthesis from CO2\/CO\/H2 over Cu\/ZnO\/Al2O3 catalyst. Korean J. Chem. Eng. 17, 332\u2013336 (2000).","journal-title":"Korean J. Chem. Eng."},{"key":"706_CR103","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1039\/D0CY00050G","volume":"10","author":"R Gaikwad","year":"2020","unstructured":"Gaikwad, R., Reymond, H., Phongprueksathat, N., Rudolf von Rohr, P. & Urakawa, A. From CO or CO2? Space-resolved insights into high-pressure CO2 hydrogenation to methanol over Cu\/ZnO\/Al2O3. Catal. Sci. Technol. 10, 2763\u20132768 (2020).","journal-title":"Catal. Sci. Technol."},{"key":"706_CR104","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.apcatb.2015.01.010","volume":"170\u2013171","author":"X Jiang","year":"2015","unstructured":"Jiang, X., Koizumi, N., Guo, X. & Song, C. Bimetallic Pd-Cu catalysts for selective CO2 hydrogenation to methanol. Appl. Catal. B 170\u2013171, 173\u2013185 (2015).","journal-title":"Appl. Catal. B"},{"key":"706_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.apcatb.2024.124198","volume":"355","author":"AJ Al Abdulghani","year":"2024","unstructured":"Al Abdulghani, A. J. et al. Realizing synergy between Cu, Ga, and Zr for selective CO2 hydrogenation to methanol. Appl. Catal. B 355, 124198 (2024).","journal-title":"Appl. Catal. B"},{"key":"706_CR106","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1038\/334577a0","volume":"334","author":"JC Frost","year":"1988","unstructured":"Frost, J. C. Junction effect interactions in methanol synthesis catalysts. Nature 334, 577\u2013580 (1988).","journal-title":"Nature"},{"key":"706_CR107","doi-asserted-by":"publisher","first-page":"132882","DOI":"10.1016\/j.fuel.2024.132882","volume":"378","author":"H Vafaee Zonouz","year":"2024","unstructured":"Vafaee Zonouz, H., Barzegari, F. & Rezaei, M. Development of CuO\/ZnO\/Al2O3-hydrotalcite-based catalysts for middle temperature water gas shift reaction: impact of calcination temperature and residual carbonates. Fuel 378, 132882 (2024).","journal-title":"Fuel"},{"key":"706_CR108","doi-asserted-by":"publisher","first-page":"27438","DOI":"10.1021\/jacs.5c04835","volume":"147","author":"AJ Al Abdulghani","year":"2025","unstructured":"Al Abdulghani, A. J. et al. Uncovering the pressure-dependent mechanism of CO2 hydrogenation to methanol on Ga-promoted Cu\/ZrO2 using operando modulation-excitation DRIFTS. J. Am. Chem. Soc. 147, 27438\u201327448 (2025).","journal-title":"J. Am. Chem. Soc."},{"key":"706_CR109","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.apcatb.2010.12.030","volume":"102","author":"SY Joshi","year":"2011","unstructured":"Joshi, S. Y., Ren, Y., Harold, M. P. & Balakotaiah, V. Determination of kinetics and controlling regimes for H2 oxidation on Pt\/Al2O3 monolithic catalyst using high space velocity experiments. Appl. Catal. B 102, 484\u2013495 (2011).","journal-title":"Appl. Catal. B"},{"key":"706_CR110","doi-asserted-by":"publisher","first-page":"101776","DOI":"10.1016\/j.surfin.2022.101776","volume":"29","author":"RM Al Soubaihi","year":"2022","unstructured":"Al Soubaihi, R. M., Saoud, K. M. & Dutta, J. Comparative investigation of structure and operating parameters on the performance and reaction dynamic of CO conversion on silica aerogel and fumed-silica-supported Pd catalysts. Surf. Interfaces 29, 101776 (2022).","journal-title":"Surf. Interfaces"},{"key":"706_CR111","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1007\/s10562-019-02772-0","volume":"149","author":"DY Murzin","year":"2019","unstructured":"Murzin, D. Y. On apparent activation energy of structure sensitive heterogeneous catalytic reactions. Catal. Lett. 149, 1455\u20131463 (2019).","journal-title":"Catal. Lett."},{"key":"706_CR112","volume":"4","author":"A Ramirez","year":"2024","unstructured":"Ramirez, A. et al. Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation. Chem. Catal. 4, 100888 (2024).","journal-title":"Chem. Catal."},{"key":"706_CR113","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/s10562-016-1960-x","volume":"147","author":"M Bukhtiyarova","year":"2017","unstructured":"Bukhtiyarova, M., Lunkenbein, T., K\u00e4hler, K. & Schl\u00f6gl, R. Methanol synthesis from industrial CO2 sources: a contribution to chemical energy conversion. Catal. Lett. 147, 416\u2013427 (2017).","journal-title":"Catal. Lett."},{"key":"706_CR114","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.chempr.2019.10.023","volume":"6","author":"Y Wang","year":"2020","unstructured":"Wang, Y. et al. Strong evidence of the role of H2O in affecting methanol selectivity from CO2 hydrogenation over Cu-ZnO-ZrO2. Chem 6, 419\u2013430 (2020).","journal-title":"Chem"},{"key":"706_CR115","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1016\/j.chempr.2024.03.027","volume":"10","author":"X Hou","year":"2024","unstructured":"Hou, X., Li, S., Frey, J., Hong, X. & Ackermann, L. Machine learning-guided yield optimization for palladaelectro-catalyzed annulation reaction. Chemistry 10, 2283\u20132294 (2024).","journal-title":"Chemistry"},{"key":"706_CR116","doi-asserted-by":"publisher","first-page":"2360928","DOI":"10.1080\/00219592.2024.2360928","volume":"57","author":"GV Huerta","year":"2024","unstructured":"Huerta, G. V., Hisama, K. & Koyama, M. A comprehensive computer-aided review of trends in heterogeneous catalysis and its theoretical modelling for engineering catalytic activity. J. Chem. Eng. Jpn. 57, 2360928 (2024).","journal-title":"J. Chem. Eng. Jpn."}],"container-title":["Communications Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s44172-026-00706-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s44172-026-00706-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s44172-026-00706-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:03:43Z","timestamp":1790035423000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s44172-026-00706-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":116,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["706"],"URL":"https:\/\/doi.org\/10.1038\/s44172-026-00706-4","relation":{},"ISSN":["2731-3395"],"issn-type":[{"value":"2731-3395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]},"assertion":[{"value":"25 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"162"}}