Orbital-resolved DFT+U for accurate predictions of Prussian blue analogues
ORAL
Abstract
Prussian blue analogues (PBAs, chemically: cyanide-bridged double perovskites) represent promising electrode materials for the production of inexpensive, durable, and non-toxic Na+ or K+ secondary batteries [1]. To optimize the performance of these materials, a careful computational understanding of atomistic processes such as ionic conduction and degradation is desirable. However, while the affordable (semi)local approximations to density-functional theory (DFT) fail at adequately describing the electronic structure of PBAs due to electrons’ self-interactions, the unfavorable scaling of more accurate high-level methods renders impossible the simulation of key intrinsic features such as hexacyanometallate defects.
Here, we show that with the recently introduced orbital-resolved Hubbard U corrections to DFT [2], key properties of PBAs including intercalation potentials can be predicted with accuracy while preserving the computational cost of standard DFT. We highlight that a pinpoint and system-specific definition of Hubbard manifolds is indispensable for this endeavor. Our results are verified against electrochemical and spectral measurements of well-characterized (XRD, ICP-OES, TGA) samples of copper-, manganese- and iron hexacyanoferrate.
[1] C. Wessells, et al., Nat. Commun. 2, 550 (2011)
[2] E. Macke, et al., JCTC 20, 4824 (2024)
Here, we show that with the recently introduced orbital-resolved Hubbard U corrections to DFT [2], key properties of PBAs including intercalation potentials can be predicted with accuracy while preserving the computational cost of standard DFT. We highlight that a pinpoint and system-specific definition of Hubbard manifolds is indispensable for this endeavor. Our results are verified against electrochemical and spectral measurements of well-characterized (XRD, ICP-OES, TGA) samples of copper-, manganese- and iron hexacyanoferrate.
[1] C. Wessells, et al., Nat. Commun. 2, 550 (2011)
[2] E. Macke, et al., JCTC 20, 4824 (2024)
*We acknowledge support by the Federal Ministry of Education and Research (BMBF) and the European Commission through the MaX Centre of Excellence for supercomputing applications (grant numbers 16HPB069/101093374)
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Presenters
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Eric Macke
- University of Bremen