The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications
Open accessAbstract
The use of hydrogen as fuel is a promising avenue to aid in the reduction of greenhouse effect gases released in the atmosphere. In this work, we present a high-throughput density functional theory (HT-DFT) study of 5,329 cubic and distorted perovskite ABO 3 compounds to screen for thermodynamically favorable two-step thermochemical water splitting (TWS) materials. From a data set of more than 11,000 calculations, we screened materials based on the following: (a) thermodynamic stability and (b) oxygen vacancy formation energy that allow favorable TWS. From our screening strategy, we identify 139 materials as potential new candidates for TWS application. Several of these compounds, such as CeCoO 3 and BiVO 3, have not been experimentally explored yet for TWS and present promising avenues for further research. We show that taking into consideration all phases present in the A–B–O ternary phase, as opposed to only calculating the formation energy of a compound, is crucial to assess correctly the stability of a compound as it reduces the number of potential candidates from 5,329 to 383. Finally, our large data set of compounds containing stabilites, oxidation states, and ionic sizes allowed us to revisit the structural maps for perovskites by showing stable and unstable compounds simultaneously.
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,638
Shannon, R. D. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001551 · Cited by 66,145
Electron-energy-loss spectra and the structural stability of nickel oxide: An LSDA+U study
Dudarev, S. L., Botton, G. A., Savrasov, S. Y., Humphreys, C. J., & Sutton, A. P. (1998) · Physical Review B · doi:10.1103/physrevb.57.1505 · Cited by 15,173
Hydrogen-storage materials for mobile applications
Schlapbach, L., & Züttel, A. (2001) · Nature · doi:10.1038/35104634 · Cited by 8,865
Effective ionic radii in oxides and fluorides
Shannon, R. D., & Prewitt, C. T. (1969) · Acta Crystallographica Section B Structural Crystallography and Crystal Chemistry · doi:10.1107/s0567740869003220 · Cited by 8,627
The quickhull algorithm for convex hulls
Barber, C. B., Dobkin, D. P., & Huhdanpaa, H. (1996) · ACM Transactions on Mathematical Software · doi:10.1145/235815.235821 · Cited by 5,359
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
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Nanoparticles: Properties, applications and toxicities
Khan, I., Saeed, K., & Khan, I. (2019) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2017.05.011 · Cited by 6,244
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
Carrillo, A. J., González-Aguilar, J., Romero, M., & Coronado, J. M. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00315 · Cited by 630
Metadata
- Authors
- Emery, A. A., Saal, J. E., Kirklin, S., Hegde, V. I., & Wolverton, C.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-07-07
- Volume
- 28
- Issue
- 16
- Pages
- 5621-5634
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2475256989
- Cited by
- 257
- References
- 71
- FWCI
- 10.49
- Type
- journal article
- Language
- en
- Open access
- green
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- Emery, A. A., Saal, J. E., Kirklin, S., Hegde, V. I., & Wolverton, C. (2016). High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications. Chemistry of Materials. https://doi.org/10.1021/acs.chemmater.6b01182
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