Monte Carlo investigation of phase changes and the order of transition of Ising modeled single-walled nanotube

Elden, A. Arul Anne; Ponmurugan, M.

doi:10.1140/epjp/s13360-022-02749-w

Cited by 3 publications

(4 citation statements)

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“…In DFT, the exact form of the exchange-correlation potential 𝑉 𝑋𝐶 in the Schrödinger equation (10) is not known and needs to be approximated. One commonly used approximation is the local density approximation (LDA), where it is assumed that the exchange-correlation energy per electron at a given point is equal to that of a homogeneous electron gas with the same density.…”

Section: Reviewmentioning

confidence: 99%

“…Nevertheless, this model continues to be widely used as a tool for investigating phase transitions through Monte Carlo simulations. [10,11] In less than a decade, a logical "extension" of the Ising formalism known as the classical Heisenberg model was proposed. This model, named after the ferromagnetic formalism proposed by Heisenberg, [6] lies between a full description of the electronic state and conventional micromagnetism.…”

Section: E (Energy)mentioning

confidence: 99%

“…Models Features Methods Examples of investigated Systems Properties 1925 Ising [8,9] Collinear MC Single-walled Phase nanotube [10] transitions (PT) 𝑇 C Fe 7 S 8 [11] 1928…”

Section: Yearmentioning

confidence: 99%

“…Although the one-and two-dimensional Ising models have analytical solutions [8,9], the three-dimensional case remains unresolved. Nevertheless, this model continues to be widely used as a tool for investigating phase transitions through Monte Carlo simulations [10,11].…”

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confidence: 99%

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Interatomic Interaction Models for Magnetic Materials: Recent Advances

Kostiuchenko,

Shapeev,

Novikov

2024

Chinese Phys. Lett.

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Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spinpolarized density functional theory (DFT) served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems. Furthermore, in recent years, a new class of interatomic potentials known as magnetic machine-learning interatomic potentials (magnetic MLIPs) has emerged. These MLIPs combine the computational efficiency, in terms of CPU time, of empirical potentials with the accuracy of DFT calculations. In this review, our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials. We also delve into the various problem classes to which these models can be applied. Finally, we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

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Section: Reviewmentioning

confidence: 99%

Section: E (Energy)mentioning

confidence: 99%

“…Models Features Methods Examples of investigated Systems Properties 1925 Ising [8,9] Collinear MC Single-walled Phase nanotube [10] transitions (PT) 𝑇 C Fe 7 S 8 [11] 1928…”

Section: Yearmentioning

confidence: 99%

mentioning

confidence: 99%

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Interatomic Interaction Models for Magnetic Materials: Recent Advances

Kostiuchenko,

Shapeev,

Novikov

2024

Chinese Phys. Lett.

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Entropy profiles of Schelling’s segregation model from the Wang–Landau algorithm

Jin

2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We evaluate Schelling’s segregation outcomes from the square lattice, regular random networks, and clustered regular random networks by situating them in the probability distribution of the entire outcome space of satisfaction and segregation. To do so, we employ the Wang–Landau algorithm and calculate the entropy and the number of states as a function of satisfaction and segregation. According to the results, satisfaction tends to increase with segregation, irrespective of the network structure. Moreover, segregation occurs almost surely when satisfaction is maximized, which we also algebraically derive and confirm on infinite-size networks. The average ratios of the neighbors of the same tag are about 67% for the square lattice and regular random networks and about 73% for clustered regular random networks with a clustering coefficient of 0.37(1). Thus, clustering increases segregation on regular random networks. Further, we find that Schelling’s path-dependent process generates sharper segregation than expected by random configurations, an outcome symptomatic of over-optimization from the social welfare perspective. Adopting an alternative rule restricting individuals’ choice set may reduce segregation without compromising on satisfaction.

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Single-walled Ising nanotube with opposite sign of interactions using Wang–Landau algorithm

Elden

Ponmurugan

2022

Eur. Phys. J. Plus

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Monte Carlo investigation of phase changes and the order of transition of Ising modeled single-walled nanotube

Cited by 3 publications

References 44 publications

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Entropy profiles of Schelling’s segregation model from the Wang–Landau algorithm

Single-walled Ising nanotube with opposite sign of interactions using Wang–Landau algorithm

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