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Dinesh Bista

Publications and source records attributed to Dinesh Bista.

2 recordsLinked to original sources
arXiv2024

Single-Phase L1$_{0}$-Ordered High Entropy Thin Films with High Magnetic Anisotropy

The vast high entropy alloy (HEA) composition space is promising for discovery of new material phases with unique properties. We explore the potential to achieve rare-earth-free high magnetic anisotropy materials in single-phase HEA thin films. Thin films of FeCoNiMnCu sputtered on thermally oxidized Si/SiO$_{2}$ substrates at room temperature are magnetically soft, with a coercivity on the order of 10 Oe. After post-deposition rapid thermal annealing (RTA), the films exhibit a single face-centered-cubic phase, with an almost 40-fold increase in coercivity. Inclusion of 50 at.% Pt in the film leads to ordering of a single L1$_{0}$ high entropy intermetallic phase after RTA, along with high magnetic anisotropy and 3 orders of magnitude coercivity increase. These results demonstrate a promising HEA approach to achieve high magnetic anisotropy materials using RTA.

cond-mat.mtrl-sci↗
arXiv2024

Fast \textit{ab initio} design of high-entropy magnetic thin films

We show that the magnetic properties of high-entropy alloys (HEAs) can be captured by \textit{ab initio} calculations within the coherent potential approximation, where the atomic details of the high-entropy mixing are considered as an effective medium that possesses the translational symmetry of the lattice. This is demonstrated using the face-centered cubic (FCC) phase of $\textrm{FeCoNiMnCu}$ and the $L1_0$ phase of $\textrm{(FeCoNiMnCu)Pt}$ by comparing the density functional theory (DFT) results with the experimental values. Working within the first Brillouin zone and the primitive unit cell, we show that DFT can capture the smooth profile of magnetic properties such as the saturation magnetization, the Curie temperature and the magnetic anisotropy, using only a sparse set of sampling points in the vast compositional space. The smooth profiles given by DFT indeed follow the experimental trend, demonstrating the promising potential of using machine learning to explore the magnetic properties of HEAs, by establishing reasonably large datasets with high-throughput calculations using density-functional theory.

cond-mat.mtrl-sci↗