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Guo, J.; Clima, S.; Pourtois, G.; Van Houdt, J. |
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Title |
Identifying alternative ferroelectric materials beyond Hf(Zr)O-₂ |
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A1 Journal article |
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Year |
2020 |
Publication |
Applied Physics Letters |
Abbreviated Journal |
Appl Phys Lett |
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Volume |
117 |
Issue |
26 |
Pages |
262903 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Abstract |
A database-driven approach combined with ab initio density functional theory (DFT) simulations is used to identify and simulate alternative ferroelectric materials beyond Hf(Zr)O-2. The database-driven screening method identifies a class of wurtzite ferroelectric materials. DFT simulations of wurtzite magnesium chalcogenides, including MgS, MgSe, and MgTe, show their potential to achieve improved ferroelectric (FE) stability, simple atomistic unit cell structure, and large FE polarization. Strain engineering can effectively modulate the FE switching barrier height for facilitating FE switching. The effect of the piezoelectric property on the FE switching barrier heights is also examined. |
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Wos |
000608049700003 |
Publication Date |
2020-12-28 |
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ISSN |
0003-6951; 1077-3118 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
4 |
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Approved |
Most recent IF: 4; 2020 IF: 3.411 |
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Call Number |
UA @ admin @ c:irua:176053 |
Serial |
6766 |
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Author |
Teunissen, J.L.; Braeckevelt, T.; Skvortsova, I.; Guo, J.; Pradhan, B.; Debroye, E.; Roeffaers, M.B.J.; Hofkens, J.; Van Aert, S.; Bals, S.; Rogge, S.M.J.; Van Speybroeck, V. |
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Title |
Additivity of Atomic Strain Fields as a Tool to Strain-Engineering Phase-Stabilized CsPbI3Perovskites |
Type |
A1 Journal Article |
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Year |
2023 |
Publication |
The Journal of Physical Chemistry C |
Abbreviated Journal |
J. Phys. Chem. C |
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Volume |
127 |
Issue |
48 |
Pages |
23400-23411 |
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A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ; |
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Abstract |
CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability. |
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001116862000001 |
Publication Date |
2023-12-07 |
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ISSN |
1932-7447 |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
3.7 |
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Open Access |
OpenAccess |
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Notes |
This work was supported by iBOF-21-085 PERsist (Special Research Fund of Ghent University, KU Leuven Research Fund, and the Research Fund of the University of Antwerp). S.M.J.R., T.B., and B.P. acknowledge financial support from the Research Foundation-Flanders (FWO) through two postdoctoral fellow- ships [grant nos. 12T3522N (S.M.J.R.) and 1275521N (B.P.)] and an SB-FWO fellowship [grant no. 1SC1319 (T.B.)]. E.D., M.B.J.R., and J.H. acknowledge financial support from the Research Foundation-Flanders (FWO, grant nos. G.0B39.15, G.0B49.15, G098319N, S002019N, S004322N, and ZW15_09- GOH6316). J.H. acknowledges support from the Flemish government through long-term structural funding Methusalem (CASAS2, Meth/15/04) and the MPI as an MPI fellow. S.V.A. and S.B. acknowledge financial support from the Research Foundation-Flanders (FWO, grant no. G0A7723N). S.M.J.R. and V.V.S. acknowledge funding from the Research Board of Ghent University (BOF). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation- Flanders (FWO) and the Flemish Government�department EWI.; KU Leuven, iBOF-21-085 PERsist ; Universiteit Antwerpen, iBOF-21-085 PERsist ; Universiteit Gent, iBOF-21-085 PERsist ; Vlaamse regering, CASAS2, Meth/15/04 ; Fonds Wetenschappelijk Onderzoek, G.0B39.15 G098319N G.0B49.15 1SC1319 12T3522N ZW15 09-GOH6316 G0A7723N 1275521N S004322N S002019N ; |
Approved |
Most recent IF: 3.7; 2023 IF: 4.536 |
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Call Number |
EMAT @ emat @c:irua:202124 |
Serial |
8985 |
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