arXiv subjects

R. He

Publications and source records attributed to R. He.

8 recordsLinked to original sources

arXiv2026

Plastic Relaxation without Dislocations in $\beta$-Ga$_2$O$_3$ Heteroepitaxy: A Structural Peculiarity of Ga$_2$O$_3$ Polymorphs

$\beta$-Ga$_2$O$_3$ grows as three-dimensional islands on mismatched c-plane sapphire, often on a thin $\alpha$-Ga$_2$O$_3$ wetting layer, and eventually forms a continuous film by island coalescence. Experiments reveal the $\beta$ film to be relaxed. Surprisingly no dislocations are observed, suggesting an unusual plastic relaxation path. We reveal the key mechanisms underlying this process by combining density functional theory, continuum nucleation theory, and molecular dynamics simulations. Plastic relaxation occurs through rearrangement of the Ga atoms beneath the oxygen plane shared by both the wetting layer and the (-201) $\beta$-Ga$_2$O$_3$ structures. A local $\alpha$ to $\beta$-relaxed phase transition is thus realized within a single plane, allowing full relaxation of the $\beta$-Ga$_2$O$_3$ island.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing Antimicrobial Molecule Prediction via Dynamic Routing Capsule Networks and Multi-Source Molecular Embeddings

Antibiotics are a vital class of drugs closely associated with the prevention and treatment of bacterial infections. Accurate prediction of molecular antimicrobial activity remains a key challenge in the pursuit of novel antibiotic candidates. However, laboratory-based antimicrobial compounds identification is costly, time-consuming, and prone to rediscovering known antibiotics, highlighting the urgent need for efficient and accurate computational models. Recent advances in machine learning (ML) and deep learning (DL) have significantly enhanced the ability to explore chemical space and identify potential antimicrobial compounds. In this study, we particularly emphasize deep learning models and employ five chemistry language models tailored for chemical data to encode small molecules. Our model incorporates a unique capsule network architecture and introduces innovations in loss function selection and feature processing modules, demonstrating superior performance in predicting inhibitory activities against Escherichia coli and Acinetobacter baumannii. We conducted a series of ablation studies to elucidate the contributions of network design and input features. Case studies validated the usability and effectiveness of our model.To facilitate accessibility, we developed an intuitive web portal to disseminate this novel tool. Our results indicate that the proposed approach offers improved predictive accuracy and enhanced interpretability, underscoring the potential of interpretable artificial intelligence methods in accelerating antibiotic discovery and addressing the urgent challenge of antimicrobial resistance.

q-bio.QM↗

arXiv2021

Electrochemical control of ferroelectricity in hafnia-based ferroelectric devices using reversible oxygen migration

Ferroelectricity, especially in hafnia-based thin films at nanosizes, has been rejuvenated in the fields of low-power, nonvolatile and Si-compatible modern memory and logic applications. Despite tremendous efforts to explore the formation of the metastable ferroelectric phase and the polarization degradation during field cycling, the ability of oxygen vacancy to exactly engineer and switch polarization remains to be elucidated. Here we report reversibly electrochemical control of ferroelectricity in Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) heterostructures with a mixed ionic-electronic LaSrMnO$_3$ electrode, achieving a hard breakdown field more than 18 MV/cm, over fourfold as high as that of typical HZO. The electrical extraction and insertion of oxygen into HZO is macroscopically characterized and atomically imaged in situ. Utilizing this reversible process, we achieved multiple polarization states and even repeatedly repaired the damaged ferroelectricity by reversed negative electric fields. Our study demonstrates the robust and switchable ferroelectricity in hafnia oxide distinctly associated with oxygen vacancy and opens up opportunities to recover, manipulate, and utilize rich ferroelectric functionalities for advanced ferroelectric functionality to empower the existing Si-based electronics such as multi-bit storage.

physics.app-ph↗

arXiv2021

Quantum engineering with hybrid magnonics systems and materials

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitatons or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

cond-mat.mes-hall↗

arXiv2010

The Atomic-scale Growth of Large-Area Monolayer Graphene on Single-Crystal Copper Substrates

We study the growth and microscopic structure of large-area graphene monolayers, grown on copper single crystals by chemical vapor deposition (CVD) in ultra-high vacuum (UHV). Using atomic-resolution scanning tunneling microscopy (STM), we find that graphene grows primarily in registry with the underlying copper lattice for both Cu(111) and Cu(100). The graphene has a hexagonal superstructure on Cu(111) with a significant electronic component, whereas it has a linear superstructure on Cu(100). The film quality is limited by grain boundaries, and the best growth is obtained on the Cu(111) surface.

cond-mat.mes-hall↗

arXiv2010

Observation of magneto-phonon resonance of Dirac fermions in graphite

Coherent coupling of Dirac fermion magneto-excitons with an optical phonon is observed in graphite as marked magnetic-field dependent splittings and anti-crossing behavior of the two coupled modes. The sharp magneto-phonon resonance occurs in regions of the graphite sample with properties of superior single-layer graphene having enhanced lifetimes of Dirac fermions. The greatly reduced carrier broadening to values below the graphene electron-phonon coupling constant explains the appearance of sharp resonances that reveal a fundamental interaction of Dirac fermions.

cond-mat.mtrl-sci↗

arXiv2008

Fermi Surface Evolution Across Multiple Charge Density Wave Transitions in ErTe3

The Fermi surface (FS) of ErTe3 is investigated using angle-resolved photoemission spectroscopy (ARPES). Low temperature measurements reveal two incommensurate charge density wave (CDW) gaps created by perpendicular FS nesting vectors. A large Delta_1 = 175 meV gap arising from a CDW with c* - q_CDW1 ~ 0.70(0) c* is in good agreement with the expected value. A second, smaller Delta_2 = 50 meV gap is due to a second CDW with a* - q_CDW2 ~ 0.68(5) a*. The temperature dependence of the FS, the two gaps and possible interaction between the CDWs are examined.

cond-mat.str-el↗

arXiv2008

ARPES Study of the Evolution of Band Structure and Charge Density Wave Properties in RTe3 for R = Y, La, Ce, Sm, Gd, Tb and Dy

We present a detailed ARPES investigation of the RTe3 family, which sets this system as an ideal "textbook" example for the formation of a nesting driven Charge Density Wave (CDW). This family indeed exhibits the full range of phenomena that can be associated to CDW instabilities, from the opening of large gaps on the best nested parts of Fermi Surface (FS) (up to 0.4eV), to the existence of residual metallic pockets. ARPES is the best suited technique to characterize these features, thanks to its unique ability to resolve the electronic structure in k-space. An additional advantage of RTe3 is that the band structure can be very accurately described by a simple 2D tight-binding (TB) model, which allows one to understand and easily reproduce many characteristics of the CDW. In this paper, we first establish the main features of the electronic structure, by comparing our ARPES measurements with Linear Muffin-Tin Orbital band calculations. We use this to define the validity and limits of the TB model. We then present a complete description of the CDW properties and, for the first time, of their strong evolution as a function of R. Using simple models, we are able to reproduce perfectly the evolution of gaps in k-space, the evolution of the CDW wave vector with R and the shape of the residual metallic pockets. Finally, we give an estimation of the CDW interaction parameters and find that the change in the electronic density of states n(Ef), due to lattice expansion when different R ions are inserted, has the correct order of magnitude to explain the evolution of the CDW properties.

cond-mat.str-el↗