activity
20242026
most citedObservation of the Axion quasiparticle in 2D MnBiTe

39 citations · 42 across the 5 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

7 papers · 1 filter

cond-mat.mtrl-sci2026

Layer-dependent antiferromagnetic Chern and axion insulating states in UOTe

Sougata Mardanya, Barun Ghosh, Mengke Liu +8

Magnetic topological insulators have received significant interest due to their dissipationless edge states, which promise advances in energy-efficient electronic transport. Howeve…

cond-mat.mtrl-sci2025

Wavefunction-Free Approach for Predicting Nonlinear Responses in Weyl Semimetals

Mohammad Yahyavi, Ilya Belopolski, Yuanjun Jin +15

By sidestepping the intractable calculations of many-body wavefunctions, density functional theory (DFT) has revolutionized the prediction of ground states of materials. However, p…

cond-mat.mtrl-sci2025

Diverse electronic topography in a distorted kagome metal LaTi3Bi4

Anup Pradhan Sakhya, Brenden R. Ortiz, Barun Ghosh +9

Recent reports on a family of kagome metals of the form LnTi3Bi4 (Ln = Lanthanide) has stoked interest due to the combination of highly anisotropic magnetism and a rich electronic…

cond-mat.mtrl-sci2025

Topological Nature of Orbital Chern Insulators

Yueh-Ting Yao, Chia-Hung Chu, Arun Bansil +2

Ground state topologies in quantum materials have unveiled many unique topological phases with novel Hall responses. Recently, the orbital Hall effect in insulators has suggested t…

cond-mat.mtrl-sci2025

Geometry-Driven Moiré Engineering in Twisted Bilayers of High-Pseudospin Fermions

Yi-Chun Hung, Xiaoting Zhou, Arun Bansil

Moiré engineering offers new pathways for manipulating emergent states in twisted layered materials and lattice-mismatched heterostructures. With the key role of the geometry of th…

cond-mat.mtrl-sci2024

Effects of Four-Phonon Scattering and Wave-like Phonon Tunneling Effects on Thermoelectric Properties of Mg2GeSe4 using Machine Learning

Hao-Jen You, Yi-Ting Chiang, Arun Bansil +1

We present a machine-learning interatomic potential (MLIP) framework, which substantially accelerates the prediction of lattice thermal conductivity for both particle-like and wave…