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20022026
most citedQuantum Computing

3.6k citations

Showing cond-mat.mtrl-sciShow all

223 papers · 1 filter

cond-mat.mtrl-sci2026★ 2 cited

Breaking symmetry to create a parallel-plate varactor dielectric with unparalleled microwave performance

Florian Bergmann, Matthew R. Barone, Zishen Tian +24

Voltage-tunable capacitors (varactors) are key to microwave circuits. Tunable dielectric varactors outperform competing technologies in almost every relevant metric but usually suf…

cond-mat.mtrl-sci2026

A phase field model with arbitrary misorientation dependence of grain boundary energy

Philip Staublin, Yuri Mishin, Peter W. Voorhees +1

Grain growth in polycrystals is often simulated using orientation-field models, which employ a field to represent the local orientation of the crystal lattice. These models can be…

cond-mat.mtrl-sci2026★ 1 cited

Quantum Kernel Machine Learning for Autonomous Materials Science

Felix Adams, Daiwei Zhu, David W. Steuerman +2

Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A cruci…

cond-mat.mtrl-sci2025★ 1 cited

Anisotropic Band-Split Magnetism in Magnetostrictive CoFeO

Harry Lane, Guratinder Kaur, Masahiro Kawamata +9

Single crystal spinel CoFeO exhibits the largest room-temperature saturation magnetostriction among non-rare-earth compounds and a high Curie temperature ( K)…

cond-mat.mtrl-sci2025

Evolution from Topological Dirac Metal to Flat-band-Induced Antiferromagnet in Layered KxNi4S2 (0<=x<=1)

Hengdi Zhao, Xiuquan Zhou, Hyowon Park +17

Condensed matter systems with coexisting Dirac cones and flat bands, and a switchable control between them within a single system, are desirable but remarkably uncommon. Here we re…

cond-mat.mtrl-sci2025★ 13 cited

DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer

Kamal Choudhary

Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This…