Impact of Bicritical Fluctuation on Magnetocaloric Phenomena in Perovskite Manganites

Sakai, Hideaki; Taguchi, Y.; Tokura, Y.

doi:10.1143/jpsj.78.113708

Cited by 15 publications

(8 citation statements)

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“…Emergent metric from experiments.-Since we have checked that the AdS Schwarzschild metric is successfully reproduced, we shall apply the deep neural network to learn a bulk geometry for a given experimental data. We use experimental data of the magnetization curve (the magnetization M [µ B /M n ] vs the external magnetic field H [Tesla]) for the 3-dimensional material Sm 0.6 Sr 0.4 MnO 3 which is known to have a strong quantum fluctuation [57], see Fig. 5.…”

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confidence: 99%

Deep learning and the AdS/CFT correspondence

et al. 2018

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We present a deep neural network representation of the AdS/CFT correspondence, and demonstrate the emergence of the bulk metric function via the learning process for given data sets of response in boundary quantum field theories. The emergent radial direction of the bulk is identified with the depth of the layers, and the network itself is interpreted as a bulk geometry. Our network provides a data-driven holographic modeling of strongly coupled systems. With a scalar φ 4 theory with unknown mass and coupling, in unknown curved spacetime with a black hole horizon, we demonstrate our deep learning (DL) framework can determine them which fit given response data. First, we show that, from boundary data generated by the AdS Schwarzschild spacetime, our network can reproduce the metric. Second, we demonstrate that our network with experimental data as an input can determine the bulk metric, the mass and the quadratic coupling of the holographic model. As an example we use the experimental data of magnetic response of a strongly correlated material Sm0.6Sr0.4MnO3. This AdS/DL correspondence not only enables gravity modeling of strongly correlated systems, but also sheds light on a hidden mechanism of the emerging space in both AdS and DL. FIG. 1: The AdS/CFT and the DL. Top: a typical view of the AdS/CFT correspondence. The CFT at a finite temperature lives at a boundary of asymptotically AdS spacetime with a black hole horizon at the other end. Bottom: a typical neural network of a deep learning.horizon condition. Therefore, a successful machine learning results in a concrete metric of a holographic modeling of the system measured by the experiment [67]. We call this implementation of the holographic model into the deep neural network as AdS/DL correspondence.We check that the holographic DL modeling nicely arXiv:1802.08313v1 [hep-th]

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confidence: 99%

Deep learning and the AdS/CFT correspondence

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“…a) TAMURA.Ryo@nims.go.jp b) OHNO.Takahisa@nims.go.jp c) KITAZAWA.Hideaki@nims.go.jp 1The importance of magnetic refrigeration has been well recognized. [1][2][3][4][5][6][7][8][9][10][11][12] In conventional ferromagnets and paramagnets, when the applying magnetic field is turned off, the magnetic entropy increases. Thus, these magnetic materials absorb an amount of heat associated with the magnetic entropy change, and the temperature drops.…”

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A generalized magnetic refrigeration scheme

Ohno

Kitazawa

2014

Applied Physics Letters

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We have investigated the magnetocaloric effects in antiferromagnets and compared them with those in ferromagnets using Monte Carlo simulations. In antiferromagnets, the magnetic entropy reaches a maximum value at a finite magnetic field when the temperature is fixed below the Néel temperature. Using the fact, we proposed a protocol for applying magnetic fields to achieve the maximum efficiency for magnetic refrigeration in antiferromagnets. In particular, we found that at low temperatures, antiferromagnets are more useful for magnetic refrigeration than ferromagnets.

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“…During such a concurrent first‐order transition, entropy release from the other degrees of freedom is expected to enhance the magnetocaloric effect. In the following sections, we will review three examples of magnetocaloric materials investigated along these lines …”

Section: Strategy To Obtain First‐order Magnetic Transitionmentioning

confidence: 99%

“…A series of R 0.6 Sr 0.4 MnO 3 ( R = rare earth atoms and combinations of them) was investigated as a system with a substantial quenched disorder . The magnetic phase diagram is summarized in Figure a as a function of tolerance factor f , which is defined as

f = (r_{normalA} + {normalr}_{normalO})/ \sqrt{2} {(r}_{Mn} + {normalr}_{normalO})

, where r A , r Mn , and r O are the ionic radii of the A ‐site cation, Mn, and oxygen, respectively.…”

Section: Cmr Manganites With Competing Charge‐orbital Ordering Instabmentioning

confidence: 99%

Magnetocaloric Materials with Multiple Instabilities

2017

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the cases of H = 0 and H ≠ 0. In the zero field, the entropy exhibits an anomaly at a ferromagnetic (or ferrimagnetic) transition temperature (T C ), where the slope changes. As a magnetic field is applied to the material at around T C , the fluctuation is suppressed and the entropy is reduced. By exploiting this effect and combining several processes to form a thermodynamic cycle, e.g., Brayton cycle (i)→(ii)→(iii)→(iv)→(i), [7] heat can be extracted from the magnetic material to the thermal bath.While there are several prerequisites (e.g., magnetic and thermal hysteresis properties, mechanical stability, etc.) for materials to be applied to magnetic refrigeration, the enhanced magnitude of the magnetocaloric effect is of primary importance. The magnitude of the magnetocaloric effect is usually evaluated as the isothermal entropy change ΔS = S(H, T) -S(H = 0, T), or adiabatic temperature change ΔT, which are illustrated in Figure 1a, for a given magnetic field change ΔH. In many cases, the isothermal entropy change ΔS is adopted to argue the magnetocaloric effect as it is easier to experimentally evaluate than the adiabatic temperature change ΔT. Here, ΔS is calculated from a series of MH curves with a fine temperature interval using the Maxwell relationRecent research on magnetocaloric materials has focused on first-order magnetic transition systems, rather than secondorder ones because of the enhanced magnitude of the magnetocaloric effect in the former class of materials. [1] In second-order transition systems, the entropy value itself is continuous as a function of temperature, and only its temperature derivative is discontinuous, as shown in the upper panel of Figure 1b. Thus, the entropy change -ΔS displays a triangular-like shape with broad tails at both the high-and low-temperature sides. In this case, the maximum value of -ΔS is generally not very large. On the other hand, in the case of the first-order transition, the entropy itself changes discontinuously at the transition temperature, and therefore, the isothermal entropy change -ΔS tends to be large in magnitude for a rather narrow range of temperatures, as illustrated in Figure 1c. At the same time, the temperature profile of -ΔS indicates a rectangular-like shape rather than the triangular shape in the case of the secondorder transition. Based on these arguments, first-order transition systems have more widely been investigated than secondorder transition systems. Representative examples showing the The magnetocaloric effect is a well-known phenomenon where the temperature of a magnetic material varies upon application or removal of a magnetic field. This effect is anticipated to be applied to magnetic refrigeration technology, which is environmentally benign. For practical applications, it is essential to explore and expand the materials horizon of novel magnets that exhibit giant magnetocaloric effects to achieve sufficient cooling efficiency. In this article, several attempts to enhance the magnetocaloric effect are reviewed from the viewpoint...

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Impact of Bicritical Fluctuation on Magnetocaloric Phenomena in Perovskite Manganites

Cited by 15 publications

References 25 publications

Deep learning and the AdS/CFT correspondence

Deep learning and the AdS/CFT correspondence

A generalized magnetic refrigeration scheme

Magnetocaloric Materials with Multiple Instabilities

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