Effect of Dy substitution in the giant magnetocaloric properties of HoB2_{2}

Recently, a massive magnetocaloric effect near the liquefaction temperature of hydrogen has been reported in the ferromagnetic material HoB$_{2}$. Here we investigate the effects of Dy substitution in the magnetocaloric properties of Ho$_{1-x}$Dy$_{x}$B$_{2}$ alloys ($\textit{x}$ = 0, 0.3, 0.5, 0.7, 1.0). We find that the Curie temperature ($\textit{T}$$_{C}$) gradually increases upon Dy substitution, while the magnitude of the magnetic entropy change |$\Delta \textit{S}_{M}$| at $\textit{T}$ = $\textit{T}_{C}$ decreases from 0.35 to 0.15 J cm$^{-3}$ K$^{-1}$ for a field change of 5 T. Due to the presence of two magnetic transitions in these alloys, despite the change in the peak magnitude of |$\Delta \textit{S}_{M}$|, the refrigerant capacity ($\textit{RC}$) and refrigerant cooling power ($\textit{RCP}$) remains almost constant in all doping range, which as large as 5.5 J cm$^{-3}$ and 7.0 J cm$^{-3}$ for a field change of 5 T. These results imply that this series of alloys could be an exciting candidate for magnetic refrigeration in the temperature range between 10-50 K.Comment: 19 pages, 5 figures, 2 table

Change in the electronic structure of the bismuth chalcogenide superconductor CsBi4-xPbxTe6 by dissociation of the bismuth dimers

CsBi4−x Pb x Te6 is synthesized and the superconductivity associated with the structural transition from Pb substitution is studied. Photoemission spectroscopy measurements are performed in order to elucidate the relationship between the electronic structure and the occurrence of the superconductivity. When Bi is substituted with Pb, an electron doping-like change in the electronic structure is directly observed which is contrary to the naive expectation of hole doping. This observation is consistent with band structure calculations and appears to be a unique characteristic of CsBi4−x Pb x Te6 because of the dissociation of Bi dimers upon Pb substitution. These results indicate that it may be possible to control the electron and hole doping via manipulating the Bi dimers through Pb substitution

Experimental exploration of ErB2_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 |{\Delta}SM|. The evaluated $|\Delta S_M|$ 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 $|\Delta S_M|$ 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.Comment: 9 pages, 10 figures. Accepted manuscript. Published by Taylor & Francis in STAM:Methods, available at https://doi.org/10.1080/27660400.2023.221747

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.Comment: 19 pages, 5 figures + 7 pages of Supporting Informatio