Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes
Creators
- 1. Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
- 2. Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, USA
- 3. Interdisciplinary Science Department, Brookhaven National Laboratory, Upton, New York 11973, USA
- 4. Applied Materials Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
- 5. Columbia Center for Computational Electrochemistry, Columbia University, New York, New York 10027, USA
- 6. Materials Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG Utrecht, The Netherlands
- 7. Columbia Electrochemical Energy Center, Columbia University, New York, New York 10027, USA
- 8. Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
* Contact person
Description
We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript. Acknowledgements: We acknowledge financial support by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Contract No. DE-SC0012704. These results used the computational resources of the Center for Functional Nanomaterials and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under the Contract No. DE-SC0012704. We also acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010.
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References
Preprint (Preprint where database is discussed) H. Guo, M. R. Carbone, C. Cao, J. Qu, Y. Du, S. Bak, C. Weiland, F. Wang, S. Yoo, N. Artrith, A. Urban, D. Lu. arXiv:2302.00126 (2023)., doi: 10.48550/arXiv.2302.00126
Journal reference (Manuscript detailing structure generation) H. Guo, Q. Wang, A. Urban, N. Artrith, Chem. Mater. 34, 6702–6712 (2022), doi: 10.1021/acs.chemmater.2c00267