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Arti Kashyap
Researcher at Indian Institute of Technology Mandi
Publications - 114
Citations - 1702
Arti Kashyap is an academic researcher from Indian Institute of Technology Mandi. The author has contributed to research in topics: Magnetization & Magnetic anisotropy. The author has an hindex of 21, co-authored 110 publications receiving 1454 citations. Previous affiliations of Arti Kashyap include Indian Institutes of Technology & ASTRON.
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Journal ArticleDOI
Predicting the Future of Permanent-Magnet Materials
TL;DR: In this paper, an analysis of aligned hard-soft nanostructures shows that soft-in-hard geometries are better than hard-insoft geometry and that embedded soft spheres are better compared to sandwiched soft layers.
Journal ArticleDOI
Inspired by nature: investigating tetrataenite for permanent magnet applications
Laura H. Lewis,A. Mubarok,Eric Poirier,N. Bordeaux,Priyanka Manchanda,Arti Kashyap,Ralph Skomski,Joseph I. Goldstein,Frederick E. Pinkerton,Raja K. Mishra,Robert C. Kubic,Katayun Barmak +11 more
TL;DR: Chemically ordered L10-type FeNi, also known as tetrataenite, is under investigation as a rare-earth-free advanced permanent magnet and preliminary results preliminarily point to a theoretical magnetic energy product exceeding (BH)max = 335 kJ m(-3) (42 MG Oe) and approaching those found in today's best rare- earth-based magnets.
Journal ArticleDOI
Electronic, magnetic and transport properties of Co2TiZ (Z=Si, Ge and Sn): A first-principle study
TL;DR: In this article, the electronic structure, magnetic and transport properties of some Co-based full Heusler alloys, namely Co 2 TiZ (Z=Si, Ge and Sn), were studied in the frame work of first-principle calculations.
Journal ArticleDOI
Interband optical properties of Ni3Al.
TL;DR: It is found that matrix elements play a significant role in influencing the magnitude of optical conductivity σ(ω) and the position of peaks.
Proceedings ArticleDOI
R-Apriori: An Efficient Apriori based Algorithm on Spark
TL;DR: A new approach is proposed which dramatically reduces this computational complexity in the implementation of Apriori by eliminating the candidate generation step and avoiding costly comparisons, and outperforms the classical A Priori and state-of-the-art on Spark by many times for different datasets.