60 research outputs found
Effect of Dy substitution in the giant magnetocaloric properties of HoB
Recently, a massive magnetocaloric effect near the liquefaction temperature
of hydrogen has been reported in the ferromagnetic material HoB. Here we
investigate the effects of Dy substitution in the magnetocaloric properties of
HoDyB alloys ( = 0, 0.3, 0.5, 0.7, 1.0). We
find that the Curie temperature () gradually increases upon
Dy substitution, while the magnitude of the magnetic entropy change || at = decreases from 0.35 to 0.15
J cm K 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 ||, the refrigerant capacity () and
refrigerant cooling power () remains almost constant in all
doping range, which as large as 5.5 J cm and 7.0 J cm 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 ErB 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
found via machine-learning predictions, we have explored the magnetocaloric
properties of a related compound ErB, that has remained the last
ferromagnetic material among the rare-earth diboride (REB) family with
unreported magnetic entropy change |{\Delta}SM|. The evaluated
at field change of 5 T in ErB turned out to be as high as 26.1 (J kg
K) around the ferromagnetic transition () of 14 K. In this
series, HoB is found to be the material with the largest 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
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