{"type":"journal_article","license":"https://creativecommons.org/licenses/by/4.0/","file":[{"date_updated":"2023-10-16T07:34:49Z","file_size":3194116,"date_created":"2023-10-16T07:34:49Z","success":1,"file_id":"14432","checksum":"7d1dffd36b672ec679f08f70ce79da87","relation":"main_file","access_level":"open_access","creator":"dernst","file_name":"2023_NatureComm_Zeng.pdf","content_type":"application/pdf"}],"day":"02","month":"10","article_processing_charge":"Yes","pmid":1,"date_updated":"2023-12-13T13:02:07Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_published":"2023-10-02T00:00:00Z","publication_status":"published","department":[{"_id":"BiCh"},{"_id":"GradSch"}],"abstract":[{"text":"Water adsorption and dissociation processes on pristine low-index TiO2 interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO2 surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO2 surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces.","lang":"eng"}],"oa_version":"Published Version","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-10-15T22:01:10Z","publisher":"Springer Nature","scopus_import":"1","title":"Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations","quality_controlled":"1","status":"public","related_material":{"link":[{"url":"https://github.com/BingqingCheng/TiO2-water","relation":"software"}]},"publication":"Nature Communications","has_accepted_license":"1","author":[{"id":"54a2c730-803f-11ed-ab7e-95b29d2680e7","full_name":"Zeng, Zezhu","first_name":"Zezhu","last_name":"Zeng"},{"last_name":"Wodaczek","first_name":"Felix","id":"8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e","full_name":"Wodaczek, Felix","orcid":"0009-0000-1457-795X"},{"last_name":"Liu","first_name":"Keyang","full_name":"Liu, Keyang"},{"last_name":"Stein","first_name":"Frederick","full_name":"Stein, Frederick"},{"full_name":"Hutter, Jürg","first_name":"Jürg","last_name":"Hutter"},{"last_name":"Chen","first_name":"Ji","full_name":"Chen, Ji"},{"orcid":"0000-0002-3584-9632","first_name":"Bingqing","last_name":"Cheng","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"doi":"10.1038/s41467-023-41865-8","external_id":{"pmid":["37783698"],"arxiv":["2303.07433"],"isi":["001084354900008"]},"publication_identifier":{"eissn":["2041-1723"]},"citation":{"ama":"Zeng Z, Wodaczek F, Liu K, et al. Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-41865-8\">10.1038/s41467-023-41865-8</a>","ista":"Zeng Z, Wodaczek F, Liu K, Stein F, Hutter J, Chen J, Cheng B. 2023. Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. Nature Communications. 14, 6131.","apa":"Zeng, Z., Wodaczek, F., Liu, K., Stein, F., Hutter, J., Chen, J., &#38; Cheng, B. (2023). Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-41865-8\">https://doi.org/10.1038/s41467-023-41865-8</a>","chicago":"Zeng, Zezhu, Felix Wodaczek, Keyang Liu, Frederick Stein, Jürg Hutter, Ji Chen, and Bingqing Cheng. “Mechanistic Insight on Water Dissociation on Pristine Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-41865-8\">https://doi.org/10.1038/s41467-023-41865-8</a>.","short":"Z. Zeng, F. Wodaczek, K. Liu, F. Stein, J. Hutter, J. Chen, B. Cheng, Nature Communications 14 (2023).","mla":"Zeng, Zezhu, et al. “Mechanistic Insight on Water Dissociation on Pristine Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.” <i>Nature Communications</i>, vol. 14, 6131, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-41865-8\">10.1038/s41467-023-41865-8</a>.","ieee":"Z. Zeng <i>et al.</i>, “Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023."},"article_type":"original","ec_funded":1,"_id":"14425","article_number":"6131","language":[{"iso":"eng"}],"intvolume":"        14","volume":14,"year":"2023","isi":1,"file_date_updated":"2023-10-16T07:34:49Z","acknowledgement":"F.S., J.H., and B.C. thank the Swiss National Supercomputing Centre (CSCS) for the generous allocation of CPU hours via production project s1108 at the Piz Daint supercomputer. B.C. acknowledges resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1. J.C. acknowledges the Beijing Natural Science Foundation for support under grant No. JQ22001. F.S., and J.H. thank the Swiss Platform for Advanced Scientific Computing (PASC) via the 2021-2024 “Ab Initio Molecular Dynamics at the Exa-Scale” project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034413.","project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","grant_number":"101034413","call_identifier":"H2020"}],"ddc":["540","000"]}