About

Arun Bansil is a University Distinguished Professor in physics at Northeastern University. He has served in various prominent roles, including managing the flagship Theoretical Condensed Matter Physics program at the US Department of Energy and founding the university’s Advanced Scientific Computation Center. Bansil has authored or co-authored over 398 technical articles and 18 volumes of conference proceedings, covering a broad range of topics in theoretical condensed matter and materials physics, including a major book on X-Ray Compton Scattering. His research has contributed significantly to the understanding of quantum materials, with recent work unveiling new phenomena in quantum mechanics and exploring revolutionary effects such as the nonlinear Hall effect in topological antiferromagnetic heterostructures. Recognized as a Highly Cited Researcher in 2017 and 2018, Bansil is also involved in cutting-edge investigations into quantum sensing, dark matter, and the fundamental properties of materials, making him a leading figure in his field.

Research topics

• Physics
• Condensed matter physics
• Mathematics
• Computer Science
• Quantum mechanics
• Statistics
• Combinatorics
• Algorithm
• Materials science

Selected publications

• Complex electronic topography and magnetotransport in an in-plane ferromagnetic kagome metal

  Physical Review Materials · 2026-05-11

  articleOpen access

  Kagome materials, with their corner-sharing triangular lattice, have attracted strong interest due to the interplay of correlations, magnetism, symmetry, and topology. Here, the authors engineer the magnetic landscape of ScMn${}_{6}$Sn${}_{6}$ via Ga doping to realize ScMn${}_{6}$(Sn${}_{0.78}$Ga${}_{0.22}$)${}_{6}$, which exhibits robust ferromagnetism below 375 K with an in-plane easy axis. High-resolution angle-resolved photoemission spectroscopy (ARPES) measurements reveal a Dirac cone near the Fermi energy, while theoretical calculations show that its gap can be tuned by the orientation of the magnetic moments. Additionally, a flat band spanning a large region of the Brillouin zone, originating from the Kagome lattice, is observed.

  PublisherOA PDFDOI

• Giant Kohn Anomaly and Chiral Phonons in the Charge-Density Wave Phase of 1H-NbSe ₂ : Impact of Phonon Anticrossing

  ACS Nano · 2026-02-17

  preprintOpen access

  Despite extensive investigations, many aspects of charge-density waves (CDWs) remain elusive, especially the relative roles of electron–phonon coupling and Fermi surface nesting as the underlying driving mechanisms responsible for the emergence of CDW vector QCDW. It is puzzling that even though electrons interact strongly with optical phonons in many correlated systems, the actual mode softening is of an acoustic mode. Here, we consider monolayer 1H-NbSe2 as an exemplar system, and through an accurate computation of the phonon self-energy, including its off-diagonal components, we provide compelling evidence that the relevant mode is a longitudinal optical phonon that softens by anticrossing several intervening phonon bands, i.e., a Kohn ladder that has been only observed previously in high-temperature superconductors. We also show that QCDW is fixed by the convolution of the susceptibility and electron–phonon coupling and that the softened phonons are circularly polarized.

  PublisherOA PDFDOI

• Structural Changes and Oxygen Dimerization in Li-Rich Layered Oxide Cathodes: An Atomic-Scale Study

  Chemistry of Materials · 2026-04-29

  article

  Li-rich transition metal oxides as cathode materials are drawing attention for their potential to dramatically increase energy storage for heavy-duty vehicles and aerospace applications. The increased capacity and energy density are achieved through additional charge compensation from oxygen at high voltages. However, the extra capacity gained from coupled cationic and anionic redox is accompanied by significant voltage fading and hysteresis with cycling. Here, we employ atomistic machine-learning methods to unravel the underlying structural changes at play in two representative Li-rich systems. We develop a machine-learning interatomic potential (MLIP) using an equivariant neural network trained on a data set generated using Density Functional Theory (DFT) for Li2Ni0.75Mn0.25O2 and Li1.2Ni0.6Mn0.2O2, both with a 3:1 Ni:Mn ratio. The training data set includes pristine as well as oxygen-bonded configurations, such as peroxide, superoxide, and O2 species, which were generated via an Ewald preconditioning scheme. Using our MLIP, we extract structural transformations driven by lithium migration from octahedral to tetrahedral sites and reveal the critical role of Mn in promoting Li occupation in locations different from the usual octahedral environment. Oxygen dimers are found to be energetically favorable only in LixNi0.6Mn0.2O2, where their formation is enabled by the presence of Li–O–Li configurations and the cation vacancies resulting from the migration of Li. Our study gives insight into the atomic-scale irreversible processes responsible for performance degradation in Li-rich layered oxide cathodes, providing a foundation for developing mitigation strategies.

  PublisherDOI

• A Predictive Framework for Designing Chiral Charge Density Wave Quantum Materials

  Research Square · 2025-07-29

  preprintOpen access
  PublisherOA PDFDOI

• Majorana Kramers pairs in synthetic high-spin Chern insulators

  Physical review. B./Physical review. B · 2025-06-04 · 1 citations

  preprintOpen accessSenior author

  High spin-Chern-number topological phases provide a promising low-dimensional platform for realizing double-helical edge states. In this letter, we show how these edge states can host a variety of phases driven by electron interaction effects, including multi-channel helical Luttinger liquid, spin density wave, superconducting phases, and a new type of $π$-junction analog of the latter two, where the transitions between the phases can be controlled. The superconducting phase in the interacting system is shown to be adiabatically connected to a time-reversal-symmetric topological superconductor in the non-interacting DIII class. This connection stabilizes Majorana Kramers pairs as domain wall states at the interface between the superconducting and $π$-spin-density wave phases, with the latter exhibiting a time-reversal-symmetric spin-density wave phase. We discuss the possibility of realizing our proposed scheme for generating Majorana Kramers pairs in a cold-atom based platform with existing techniques, and how it could offer potential advantages over other approaches.

  PublisherOA PDFDOI

• Geometry-driven moiré engineering in twisted bilayers hosting high-pseudospin fermions

  Physical review. B./Physical review. B · 2025-06-30 · 1 citations

  articleOpen accessSenior author

  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 the underlying lattice in mind, here we introduce the watermill lattice, a two-dimensional structure with low-energy states characterized by massless pseudospin-3/2 fermions with high winding numbers. Its twisted bilayer is shown to exhibit magic angles, where four isolated flat bands emerge around the Fermi level, featuring elevated Wilson-loop windings and enhanced quantum geometric effects, such as an increase in the ratio of the Berezinskii-Kosterlitz-Thouless (BKT) transition temperature to the mean-field critical temperature under a weak Bardeen-Cooper-Schrieffer (BCS) pairing. We discuss how the watermill lattice could be realized in the MXene and group-IV materials. Our study highlights the potential of exploiting lattice geometry in moiré engineering to uncover novel quantum phenomena and tailor emergent electronic properties in materials.

  PublisherOA PDFDOI

• An Agentic Artificially Intelligent X-ray Scientist

  Research Square · 2025-08-27

  preprintOpen access
  PublisherOA PDFDOI

• Flux Channeling Induced Nanoconfinement and Enhancement of Microwaves Imaged by Rabi Oscillation Mapping

  Nano Letters · 2025-06-03 · 1 citations

  articleOpen access

  With rapid advances in qubit technologies, techniques for localizing, modulating, and measuring RF fields and their impact on qubit performance are of the utmost importance. Here, we demonstrate that flux-channeling from a permalloy nanowire can be used to achieve localized spatial modulation of an RF field and that the modulated field can be mapped with high resolution by using the Rabi oscillations of an NV center. Rabi maps reveal ∼100 mm wavelength microwaves concentrated in sub-300 nm regions with up to ∼16× power enhancement. This modulation is robust over a 20 dBm power range and has no adverse impact on NV T2 coherence time. Micromagnetic simulations confirm that the modulated field results from the nanowire’s stray field through its constructive/destructive interference with the incident RF field. Our findings provide a new pathway for controlling qubits, amplifying RF signals, and mapping local fields in various on-chip RF technologies.

  PublisherDOI

• Physics-guided dual implicit neural representations for source separation

  Machine Learning Science and Technology · 2025-10-17

  articleOpen access

  Abstract Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

  PublisherDOI

• Physics-Guided Dual Implicit Neural Representations for Source Separation

  ArXiv.org · 2025-07-07

  articleOpen access

  Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions--such as background and signal distortions--that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated as well as experimental momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

  PublisherOA PDF

Frequent coauthors

• B. Barbiellini

  493 shared

• Hsin Lin

  352 shared

• R. S. Markiewicz

  Universidad del Noreste

  199 shared

• K. Pussi

  Natural Resources Institute Finland

  175 shared

• Koji Ohara

  Japan Synchrotron Radiation Research Institute

  172 shared

• Hiroki Yamada

  Japan Synchrotron Radiation Research Institute

  167 shared

• M. Zahid Hasan

  124 shared

• Bahadur Singh

  122 shared

Labs

• Experiential Quantum Advancement LaboratoriesPI

Awards & honors

• Highly Cited Researcher (2017, 2018)
• Four Northeastern Professors Named to 2021's List of 'Highly…
• Four Northeastern Researchers Named to 2020 List of 'Highly…
• Six Northeastern Professors Named to 2019 List of 'Highly Ci…