arXiv subjects

Takafumi D Yamamoto

Publications and source records attributed to Takafumi D Yamamoto.

5 recordsLinked to original sources

arXiv2024

Pressure-induced anomalous enhancement in superconducting critical temperature of transition-metal chalcogenide Ta$_2$PdS$_6$ and Ta$_2$PdSe$_6$

The emergence of a second dome in the superconducting phase through pressure-driven manipulation of crystal structures in materials has attracted considerable attention. Transition metal chalcogenides (TMCs) represent a highly promising platform, as the second dome has been observed in several binary compounds. Recently, ternary TMCs such as Ta$_2$PdS$_6$ and Ta$_2$PdSe$_6$ have exhibited pressure-induced superconducting domes. In this study, we perform electrical transport measurements of Ta$_2$PdS$_6$ and Ta$_2$PdSe$_6$ under extremely high pressures exceeding 100 GPa, namely uninvestigated regions in previous reports, to reveal the emergence of the second dome. The superconducting critical temperatures (T$_c$) in both Ta$_2$PdS$_6$ and Ta$_2$PdSe$_6$ initially decrease with increasing pressure. Subsequently, the Tcs tend to enhance drastically above 100 GPa. Notably, the maximum T$_c$ in Ta$_2$PdS$_6$ is 11.2 K at 130.0 GPa, which is a relatively high record among the TMCs. The emergence of the second dome in Ta$_2$PdS$_6$ and Ta$_2$PdSe$_6$ opens further motivation for the investigation under extreme conditions beyond the first dome to find hidden ordered phases.

cond-mat.supr-con↗

arXiv2023

Experimental exploration of ErB$_2$ and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design

Stimulated by a recent report of a giant magnetocaloric effect in HoB$_2$ found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB$_2$, that has remained the last ferromagnetic material among the rare-earth diboride (REB$_2$) family with unreported magnetic entropy change |ΔSM|. The evaluated $|ΔS_M|$ at field change of 5 T in ErB$_2$ turned out to be as high as 26.1 (J kg$^{-1}$ K$^{-1}$) around the ferromagnetic transition (${T_C}$) of 14 K. In this series, HoB$_2$ is found to be the material with the largest $|ΔS_M|$ as the model predicted, while the predicted values showed a deviation with a systematic error compared to the experimental values. Through a coalition analysis using SHAP, we explore how this rare-earth dependence and the deviation in the prediction are deduced in the model. We further discuss how SHAP analysis can be useful in clarifying favorable combinations of constituent atoms through the machine-learned model with compositional descriptors. This analysis helps us to perform materials design with aid of machine learning of materials data.

cond-mat.mtrl-sci↗

arXiv2022

Neural networks for a quick access to a digital twin of scanning physical properties measurements

For performing successful measurements within limited experimental time, efficient use of preliminary data plays a crucial role. This work shows that a simple feedforward type neural networks approach for learning preliminary experimental data can provide quick access to simulate the experiment within the learned range. The approach is especially beneficial for physical properties measurements with scanning on multiple axes, where derivative or integration of data are required to obtain the objective quantity. Due to its simplicity, the learning process is fast enough for the users to perform learning and simulation on-the-fly by using a combination of open-source optimization techniques and deep-learning libraries. Here such a tool for augmenting the experimental data is proposed, aiming to help researchers to decide the most suitable experimental conditions before performing costly experiments in real. Furthermore, this tool can also be used from the perspective of taking advantage of reutilizing and repurposing previously published data, accelerating data-driven exploration of functional materials.

cond-mat.mtrl-sci↗

arXiv2020

Enhancement of giant refrigerant capacity in Ho$_{1-x}$Gd$_{x}$B$_{2}$ alloys (0.1 $\leq$ x $\leq$ 0.4)

Intending to optimize the giant magnetocaloric properties of HoB$_{2}$, we synthesized and magnetocalorically characterized Ho$_{1-x}$Gd$_{x}$B$_{2}$(0.1 $\leq$ x $\leq$ 0.4) alloys. We found out that Gd enters stoichiometrically and randomly into the Ho site, leading to a Vegard-type structural change. The addition of spherical S$^{7/2}$ Gd$^{3+}$ moments prompts an enhancement in Curie temperature, a reduction in peak value of the magnetic entropy change while still being relatively high, and a broadening of the magnetic entropy change curves. The overall influence is a relatively high refrigerant capacity and relative cooling power, and an extension of the thermal working range to higher temperatures; thus, electing Ho$_{1-x}$Gd$_{x}$B$_{2}$(as potential candidates for cryogenic refrigeration applications.

cond-mat.mtrl-sci↗

arXiv2020

Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature

Magnetic refrigeration exploits the magnetocaloric effect which is the entropy change upon application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than the conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials which exhibits large magnetocaloric effect, these properties for a large number of compounds still remain unknown. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials which could exhibit large magnetocaloric effect. By this approach, HoB$_{2}$ is singled out, synthesized and its magnetocaloric properties are evaluated, leading to the experimental discovery of gigantic magnetic entropy change 40.1 J kg$^{-1}$ K$^{-1}$ (0.35 J cm$^{-3}$ K$^{-1}$) for a field change of 5 T in the vicinity of a ferromagnetic second-order phase transition with a Curie temperature of 15 K. This is the highest value reported so far, to our knowledge, near the hydrogen liquefaction temperature thus it is a highly suitable material for hydrogen liquefaction and low temperature magnetic cooling applications.

cond-mat.mtrl-sci↗