Water dissociation at the water–rutile TiO 2 (110) interface from ab initio-based deep neural network simulations

Wen, Bo; Calegari Andrade, Marcos F.; Liu, Li-Min; Selloni, Annabella

doi:10.1073/pnas.2212250120

Title: Water dissociation at the water–rutile TiO ₂ (110) interface from ab initio-based deep neural network simulations

Journal Article · Wed Jan 04 00:00:00 EST 2023 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.2212250120· OSTI ID:1907730

^[1]; Calegari Andrade, Marcos F. ^[2]; Liu, Li-Min ^[3];

^[2]

School of Physics and Electronics, Henan University, Kaifeng 475001, People’s Republic of China
Department of Chemistry, Princeton University, Princeton, NJ 08544
School of Physics, Beihang University, Beijing 100083, People’s Republic of China

The interaction of water with TiO ₂ surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO ₂ –water interface has a critical role in the surface chemistry of TiO ₂ , but is difficult to determine both experimentally and computationally. Among TiO ₂ surfaces, rutile TiO ₂ (110) is of special interest as the most abundant surface of TiO ₂ ’s stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO ₂ (110) with gas-phase water, much less is known about the TiO ₂ (110)–water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO ₂ (110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO ₂ (110) interactions over a wide range of water coverages. Simulations on TiO ₂ (110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns . These quantities are both much larger than corresponding estimates for the aqueous anatase TiO ₂ (101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.

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Research Organization:: Princeton Univ., NJ (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: SC0007347; SC0019394; AC02-05CH11231

OSTI ID:: 1907730

Alternate ID(s):: OSTI ID: 1994175

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 120 Journal Issue: 2; ISSN 0027-8424

Publisher:: Proceedings of the National Academy of SciencesCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
water/metal oxide interfaces
deep neural network potentials
molecular dynamics simulations
TiO2

Title: Water dissociation at the water–rutile TiO 2 (110) interface from ab initio-based deep neural network simulations

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Title: Water dissociation at the water–rutile TiO ₂ (110) interface from ab initio-based deep neural network simulations