Theoretical exploration of mixed-anion antiperovskite semiconductors 
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Mochizuki, Yasuhide; Sung, Ha-Jun; Takahashi, Akira; Kumagai, Yu; Oba, Fumiyasu

doi:10.1103/physrevmaterials.4.044601

Cited by 30 publications

(21 citation statements)

References 75 publications

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“…Finally, the calculated ground-state structure and ε ion are sensitive to the effective U (U eff ) parameter. Investigating the ground-state structure of PbTiO 3 by imaginary phonon mode analysis [62,63], we found that the most stable structure with U eff (Ti) = 3 eV is rhombohedral (R3c), which is not available in the Materials Project database and inconsistent with the experimentally observed tetragonal structure at low temperature [64]. However, when U eff (Ti) is set to 0 eV, the space group of the theoretical ground-state structure of PbTiO 3 is P4mm and ε ion decreases to 103.9.…”

Section: Compoundmentioning

confidence: 99%

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Takahashi

Kumagai

Miyamoto

et al. 2020

Phys. Rev. Materials

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Prediction models of both the electronic and ionic contributions to the static dielectric constants have been constructed using data from density functional perturbation theory calculations of approximately 1200 metal oxides via supervised machine learning. We developed two types of random forest regression models for oxides with the ground-state crystal structures: one model requires only compositional information and the other model also uses structural information. Although the training data included various atomic frameworks, the prediction models performed well even when only compositional information was used as feature descriptors. In prediction of the electronic contributions to the dielectric constants, the accuracies of the regression models with and without structural information were comparable, while the structural descriptors more clearly improved the prediction accuracy for the ionic contributions. We also analyzed the feature importance for prediction of the dielectric constants. The mean atomic mass and mass density were determined to be significant features in prediction of the electronic contributions without and with structural information, respectively. The standard deviation of the principal quantum number and mean neighbor distance variation were found to be important for the respective prediction models of the ionic contributions. The correlations between the dielectric constants and these features are discussed, along with the underlying physical mechanisms.

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Section: Compoundmentioning

confidence: 99%

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Takahashi

Kumagai

Miyamoto

et al. 2020

Phys. Rev. Materials

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“…[28][29][30][31] Group theory serves as an important tool to interpret the underlying symmetry relations of crystal structures. [32][33][34][35][36][37] For example, Howard and Stokes exhausted the space groups accessible from cubic perovskites through rigid octahedral tilting. 38 More recently, Wagner et al analysed density functional theory (DFT) results using a combination of group theory and statistical correlation analysis and showed the efficacy of combining these techniques.…”

Section: Introductionmentioning

confidence: 99%

Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals

et al. 2022

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While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures.We are interested in a special case, where distortions emerge relative to an ideal highsymmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to a dataset of 552 octahedra in ABO 3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO 3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI 3 are well described by this approach.

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“…15,19 On the contrary, several reported all-inorganic antiperovskite semiconductors X3NA (X 2+ = Ca 2+ , Mg 2+ ; A 3-= P 3-, Sb 3-, Bi 3-) possess suitable direct band gaps and high optical absorption coefficients, indicating that they are promising photovoltaic absorbers. 17,18,20 Exploring the composition-property relationships are very important for understanding and designing photovoltaic materials with excellent performance. 21 For conventional ABX3 perovskites, it is well known that the B-and X-site anions have major impacts on the electronic structures, while A-site cation does not contribute the band edge because of its highly ionic nature.…”

Section: Introductionmentioning

confidence: 99%

Structure–Composition–Property Relationships in Antiperovskite Nitrides: Guiding a Rational Alloy Design

Zhong

Feng

Wang

et al. 2021

ACS Appl. Mater. Interfaces

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ABX3 perovskites have attracted intensive research interest in recent years due to their versatile composition and superior optoelectronic properties. Their counterparts, antiperovskites (X3BA), can be viewed as electronically inverted perovskite derivatives, but they have not been extensively studied for solar applications. Therefore, understanding their composition-property relationships is crucial for future photovoltaic application. Here, taking six antiperovskite nitrides +2 3-3-3 X N A (X = Mg, Ca, Sr; A = P, As, Sb, Bi) as an example, we investigate the effect of X-and A-sites on the electronic, dielectric, and mechanical properties from the viewpoint of the first-principles calculations. Our calculation results show that the X-site dominates the conduction band, and the A-site has a non-negligible contribution to the band edge. These findings are completely different from traditional halide perovskites.Interestingly, when changing X-or A-site elements, a linear relationship between the tolerance factor and physical quantities, such as electronic parameters, dielectric constants, and Young's modulus, is observed. By designing the Mg3NAs1-xBix alloys, we further verify this power of the linear relationship, which provides a predictive guidance for experimental preparation of antiperovskite alloys. Finally, we make a comprehensive comparison between the antiperovskite nitrides and conventional halide perovskites for pointing out the future device applications.

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Theoretical exploration of mixed-anion antiperovskite semiconductors M3XN(M=Mg,Ca,Sr,Ba;X

Cited by 30 publications

References 75 publications

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals

Structure–Composition–Property Relationships in Antiperovskite Nitrides: Guiding a Rational Alloy Design

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