Predicting New TiO2 Phases with Low Band Gaps by a Multiobjective Global Optimization Approach
Abstract
TiO 2 has been extensively studied due to the possible application in solar cells and photoelectrochemical (PEC) water-splitting. However, the energy conversion efficiency is rather low because of the large band gaps (larger than 3.0 eV) of rutile and anatase TiO 2 . Here we introduce the multiobjective differential evolution (MODE) method as a novel global optimization algorithm to predict new polymorphs of bulk TiO 2 with better optical properties than rutile and anatase TiO 2 . The band gaps of the new PI ( Pnma ) and CI (C2) phases are found to be 1.95 and 2.64 eV. The calculation of formation energy, phonon dispersions, and thermal stability shows that the two novel phases are dynamically and thermally stable. These new TiO 2 polymorphs with better electronic and optical properties may pave a new way for high-efficiency solar energy conversion.
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- Authors
- Chen, H.-Z., Zhang, Y.-Y., Gong, X., & Xiang, H.
- Affiliations
- Fudan University (CN)
- Key Laboratory of Computational Physical Sciences (Ministry of Education), State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai 200433, P. R. China
- Corresponding author
- Xiang, Hongjun
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-01-23
- Volume
- 118
- Issue
- 5
- Pages
- 2333-2337
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W2144112951
- Cited by
- 40
- References
- 31
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 6 · 2023: 5 · 2022: 4 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 2 · 2017: 4 · 2016: 2 · 2015: 3 · 2014: 2
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Physical Chemistry C (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › TiO2 Photocatalysis and Solar Cells
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- TiO2 polymorphs, low band gap, multi-objective differential evolution, global optimization, Pnma phase, rutile, anatase, formation energy, phonon dispersion, thermal stability, solar cells, photoelectrochemical water splitting, optical properties
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