Oxygen Vacancy Diffusion in Rutile TiO2: Insight from Deep Neural Network Potential Simulations
- Zhihong Wu
Zhihong WuKey Laboratory for Special Functional Materials of Ministry of Education, School of Materials Science and Engineering, Henan University, Kaifeng 475004, ChinaMore by Zhihong Wu
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- Wen-Jin Yin
Wen-Jin YinSchool of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan 411201, ChinaMore by Wen-Jin Yin
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- Bo Wen*
Bo Wen*Email: [email protected]School of Physics and Electronics, Henan University, Kaifeng 475004, ChinaMore by Bo Wen
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- Dongwei Ma*
Dongwei Ma*Email: [email protected]Key Laboratory for Special Functional Materials of Ministry of Education, School of Materials Science and Engineering, Henan University, Kaifeng 475004, ChinaMore by Dongwei Ma
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- Li-Min Liu*
Li-Min Liu*Email: [email protected]School of Physics, Beihang University, Beijing 100083, ChinaMore by Li-Min Liu
Abstract
Defects play a crucial role in the surface reactivity and electronic engineering of titanium dioxide (TiO2). In this work, we have used an active learning method to train deep neural network potentials from the ab initio data of a defective TiO2 surface. Validations show a good consistency between the deep potentials (DPs) and density functional theory (DFT) results. Therefore, the DPs were further applied on the extended surface and executed for nanoseconds. The results show that the oxygen vacancy at various sites are very stable under 330 K. However, some unstable defect sites will convert to the most favorable ones after tens or hundreds of picoseconds, while the temperature was elevated to 500 K. The DP predicated barriers of oxygen vacancy diffusion were similar to those of DFT. These results show that machine-learning trained DPs could accelerate the molecular dynamics with a DFT-level accuracy and promote people’s understanding of the microscopic mechanism of fundamental reactions.
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