Cluster Expansion Framework for the Sr(Ti1–xFex)O3–x/2 (0 &lt; x &lt; 1) Mixed Ionic Electronic Conductor: Properties Based on Realistic Configurations

Ouyang, Bin; Chakraborty, Tanmoy; Kim, Namhoon; Perry, Nicola H.; Mueller, Tim; Aluru, N. R.; Ertekin, Elif

doi:10.1021/acs.chemmater.8b04285

Cited by 9 publications

(4 citation statements)

References 67 publications

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“…13 Such disagreement between the model and the measurement results was explained by oxygen vacancy ordering, typical of highly nonstoichiometric SrFeO 3– δ (ref. 13) as well as similar oxides such as SrFe 1– x Ti x O 3– δ , 141,142 impacting ε chem values. This conclusion is qualitatively supported by the DFT simulation results, showing increasing oxygen vacancy polaron size with increasing δ in Sr 1– x La x FeO 3– δ due to the vacancy ordering.…”

Section: Various Models and Approaches To Understand The Chemical Exp...mentioning

confidence: 99%

Chemical lattice strain in nonstoichiometric oxides: an overview

et al. 2022

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Section: Various Models and Approaches To Understand The Chemical Exp...mentioning

confidence: 99%

Chemical lattice strain in nonstoichiometric oxides: an overview

et al. 2022

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“…However, recently, it was shown that the SQS configurations are not ground states, as more stable configurations for disordered systems were identified via cluster expansion (CE) Monte Carlo (MC) simulations. 16 Yet, it remains unclear whether the reported model accuracy of CE is sufficient for configurational optimization. For example, considering a large supercell with 300 atoms, which is typically needed to mimic the randomness of disordered perovskites, 15 the model's prediction error of 5 meV/atom would lead to the deviation of the estimated total energy of 1.5 eV.…”

Section: Introductionmentioning

confidence: 99%

“…These SQS configurations were found to satisfactorily reproduce many material properties. However, recently, it was shown that the SQS configurations are not ground states, as more stable configurations for disordered systems were identified via cluster expansion (CE) Monte Carlo (MC) simulations . Yet, it remains unclear whether the reported model accuracy of CE is sufficient for configurational optimization.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping

Zhang,

Li,

Han

et al. 2024

J. Chem. Theory Comput.

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Cation ordering in multication perovskites is related to many important material properties and performances, but computational determination of the cation ordering remains a major challenge. Here, we propose a new computational approach by introducing a machine learning recommender system into the basin-hopping framework (RBH) for optimizing cation ordering. Taking the electrocatalyst Ba 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3 (BSCF5582) as a showcase example, we found that the efficiency of RBH in identifying low-energy configurations outperforms the methods of cluster expansion and conventional basin-hopping. The RBH results revealed that the BSCF5582 catalyst tended to have a layered ordering of A-site cations and disordered B-site cations both in bulk and on the surfaces. Further, on the A-site-terminated surface, we found the segregation of large Ba atoms. Similarly, on the A-site-terminated surface of the recently developed Cs 0.2 Sr 0.8 Co 0.4 Fe 0.6 O 3 (CSCF2846) catalyst, layered ordering at the A-site and surface enrichment of large Cs atoms were also found. The layered ordering was robust against thermal effects, as found from molecular dynamics simulations at 800 K. This work provides a new approach for thermodynamic global optimization of chemical ordering in multicomponent materials.

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“…To this end, we use alloy cluster expansions (CEs) fitted to DFT to describe the energetics of the system ranging from 0% to 50% Au in Pd and in the full range from no hydrogen to 100% hydrogen (i.e., 1 H atom per 1 Au or Pd atom). This approach, which has been successfully applied to similar materials in the past [30][31][32][33][34], allows for very fast evaluation of the energy of a system with hundreds of atoms with an accuracy approaching that of DFT. Using Monte Carlo (MC) simulations, we can elucidate the thermodynamics and, in particular, study how chemical order evolves under different circumstances.…”

Section: Introductionmentioning

confidence: 99%

A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd-Au-H

Rahm,

Löfgren,

Fransson

et al. 2021

Preprint

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Due to their ability to reversibly absorb/desorb hydrogen without hysteresis, Pd-Au nanoalloys have been proposed as materials for hydrogen sensing. For sensing, it is important that absorption/desorption isotherms are reproducible and stable over time. A few studies have pointed to the influence of short and long range chemical order on these isotherms, but many aspects of the impact of chemical order have remained unexplored. Here, we use alloy cluster expansions to describe the thermodynamics of hydrogen in Pd-Au in a wide concentration range. We investigate how different chemical orderings, corresponding to annealing at different temperatures as well as different external pressures of hydrogen, impact the behavior of the material with focus on its hydrogen absorption/desorption isotherms. In particular, we find that a long-range ordered L12 phase is expected to form if the H2 pressure is sufficiently high. Furthermore, we construct the phase diagram at temperatures from 250 K to 500 K, showing that if full equilibrium is reached in the presence of hydrogen, phase separation can often be expected to occur, in stark contrast to the phase diagram in para-equilibrium. Our results explain the experimental observation that absorption/desorption isotherms in Pd-Au are often stable over time, but also reveal pitfalls for when this may not be the case.

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Cluster Expansion Framework for the Sr(Ti_1–xFe_x)O_3–x/2 (0 < x < 1) Mixed Ionic Electronic Conductor: Properties Based on Realistic Configurations

Cited by 9 publications

References 67 publications

Chemical lattice strain in nonstoichiometric oxides: an overview

Chemical lattice strain in nonstoichiometric oxides: an overview

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping

A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd-Au-H

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