日本物理学会誌及びJournal of the Physical Society of Japanの月刊について
01 Jan 1955-Vol. 10, Iss: 3, pp 61
About: The article was published on 1955-01-01 and is currently open access. It has received 2246 citations till now.
Citations
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TL;DR: This code works in the tight-binding framework, which can be generated by another software package Wannier90 Mostofi et al. (2008), and can help to classify the topological phase of a given materials by calculating the Wilson loop, and get the surface state spectrum.
1,566 citations
Cites methods from "日本物理学会誌及びJournal of the Physical So..."
...There are several methods [33, 24, 25] to calculate the Z2 number in inversion symmetry breaking systems....
[...]
TL;DR: In this paper, a review of the recent theoretical and experimental advances in the study of ultra-cold gases made of bosonic particles interacting via the long-range, anisotropic dipole-dipole interaction, in addition to the short-range and isotropic contact interaction usually at work in ultracold gases is presented.
Abstract: This paper reviews the recent theoretical and experimental advances in the study of ultra-cold gases made of bosonic particles interacting via the long-range, anisotropic dipole–dipole interaction, in addition to the short-range and isotropic contact interaction usually at work in ultra-cold gases. The specific properties emerging from the dipolar interaction are emphasized, from the mean-field regime valid for dilute Bose–Einstein condensates, to the strongly correlated regimes reached for dipolar bosons in optical lattices. (Some figures in this article are in colour only in the electronic version)
1,230 citations
Book•
27 Sep 2001
1,221 citations
TL;DR: A crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials.
Abstract: The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
1,202 citations
Cites background from "日本物理学会誌及びJournal of the Physical So..."
...We discovered 33 stable perovskites (Table S5) out of 378 where many of the compounds like PbTiO3[20], PbZrO3[20], and SnTaO3[21] have been experimentally synthesized....
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...Many of these compounds like PbTiO3[28], PbZrO3[28], SnTaO3[29], and PbMoO3[30] have been experimentally synthesized....
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TL;DR: In this article, a review of pedagogically non-Abelian discrete groups, which play an important role in the particle physics, is presented, and the authors show group-theoretical aspects for many concrete groups, such as representations, characters, representations, and tensor products.
Abstract: We review pedagogically non-Abelian discrete groups, which play an important role in the particle physics. We show group-theoretical aspects for many concrete groups, such as representations, their tensor products. We explain how to derive, conjugacy classes, characters, representations, and tensor products for these groups (with a finite number). We discussed them explicitly for $S_N$, $A_N$, $T'$, $D_N$, $Q_N$, $\Sigma(2N^2)$, $\Delta(3N^2)$, $T_7$, $\Sigma(3N^3)$ and $\Delta(6N^2)$, which have been applied for model building in the particle physics. We also present typical flavor models by using $A_4$, $S_4$, and $\Delta (54)$ groups. Breaking patterns of discrete groups and decompositions of multiplets are important for applications of the non-Abelian discrete symmetry. We discuss these breaking patterns of the non-Abelian discrete group, which are a powerful tool for model buildings. We also review briefly about anomalies of non-Abelian discrete symmetries by using the path integral approach.
950 citations
Cites background or methods from "日本物理学会誌及びJournal of the Physical So..."
...Following [152], we label the vertex by number n = 1, · · · , 12 in Figure 4....
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...Thus, it is pedagogical to explain group-theoretical aspects of A5 as the symmetry of a regular icosahedron [152]....
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...(77) The generators, s and t, are represented as [152],...
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References
More filters
TL;DR: This code works in the tight-binding framework, which can be generated by another software package Wannier90 Mostofi et al. (2008), and can help to classify the topological phase of a given materials by calculating the Wilson loop, and get the surface state spectrum.
1,566 citations
TL;DR: In this paper, a review of the recent theoretical and experimental advances in the study of ultra-cold gases made of bosonic particles interacting via the long-range, anisotropic dipole-dipole interaction, in addition to the short-range and isotropic contact interaction usually at work in ultracold gases is presented.
Abstract: This paper reviews the recent theoretical and experimental advances in the study of ultra-cold gases made of bosonic particles interacting via the long-range, anisotropic dipole–dipole interaction, in addition to the short-range and isotropic contact interaction usually at work in ultra-cold gases. The specific properties emerging from the dipolar interaction are emphasized, from the mean-field regime valid for dilute Bose–Einstein condensates, to the strongly correlated regimes reached for dipolar bosons in optical lattices. (Some figures in this article are in colour only in the electronic version)
1,230 citations
Book•
27 Sep 2001
1,221 citations
TL;DR: A crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials.
Abstract: The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
1,202 citations
TL;DR: In this article, a review of pedagogically non-Abelian discrete groups, which play an important role in the particle physics, is presented, and the authors show group-theoretical aspects for many concrete groups, such as representations, characters, representations, and tensor products.
Abstract: We review pedagogically non-Abelian discrete groups, which play an important role in the particle physics. We show group-theoretical aspects for many concrete groups, such as representations, their tensor products. We explain how to derive, conjugacy classes, characters, representations, and tensor products for these groups (with a finite number). We discussed them explicitly for $S_N$, $A_N$, $T'$, $D_N$, $Q_N$, $\Sigma(2N^2)$, $\Delta(3N^2)$, $T_7$, $\Sigma(3N^3)$ and $\Delta(6N^2)$, which have been applied for model building in the particle physics. We also present typical flavor models by using $A_4$, $S_4$, and $\Delta (54)$ groups. Breaking patterns of discrete groups and decompositions of multiplets are important for applications of the non-Abelian discrete symmetry. We discuss these breaking patterns of the non-Abelian discrete group, which are a powerful tool for model buildings. We also review briefly about anomalies of non-Abelian discrete symmetries by using the path integral approach.
950 citations