arXiv subjects

E Zhou

Publications and source records attributed to E Zhou.

7 recordsLinked to original sources

arXiv2026

Anisotropic Kinetics of Ion-Irradiation-Induced Phase Transition in Gallium Oxide

Radiation-tolerant semiconductors have traditionally been engineered by the principle of suppressing defect accumulation and amorphization, based on the assumption that radiation damage is inherently stochastic. Here we show that, in monoclinic $\beta$-\ce{Ga2O3}, a promising ultrawide-bandgap semiconductor, surface crystallographic orientation deterministically governs radiation tolerance through highly anisotropic kinetics of the $\beta$-to-$\gamma$ phase transition. Using machine-learning molecular dynamics coupled with a local configurational-entropy descriptor, we quantitatively map anisotropic $\beta$-to-$\gamma$ transition kinetics, showing that the critical dose, transition-layer depth, and kinetic stability of the $\gamma$-phase are fundamentally governed by surface orientation. Under ion irradiation, non-channeling surfaces such as (100), (001), and (-201) undergo severe surface amorphization, whereas the strongly channeling (010) surface resists damage accumulation and promotes subsurface $\gamma$-phase nucleation. During thermal annealing recovery process, these initial states follow two distinct recovery pathways: the channeling (010) surface reverts directly from $\gamma$-to-$\beta$, whereas non-channeling surfaces follow a sequential amorphous-to-$\gamma$-to-$\beta$ transition pathway. This work establishes surface orientation as a fundamental design principle for achieving radiation tolerance through controlled polymorphic transitions, providing a universal framework for engineering functional materials capable of withstanding extreme irradiation environments.

cond-mat.mtrl-sci↗

arXiv2022

Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author

physics.comp-ph↗

arXiv2022

Electrically driven robust tuning of lattice thermal conductivity

Two-dimensional (2D) materials represented by graphene stand out in future electrical industry and have been widely studied. As a commonly existing factor in electronic devices, the electric field has been extensively utilized to modulate the performance. However, how the electric field regulates thermal transport is rarely studied. Herein, we investigate the modulation of thermal transport properties by applying the external electric field ranging from 0 to 0.4 VA-1, with bilayer graphene, monolayer silicene, and germanene as study cases. The monotonic decreasing trend of thermal conductivity of all the three materials is revealed. The significant effect on the scattering rate is found to be responsible for the decreased thermal conductivity by electric field. Further evidences show that the reconstruction of internal electric field and the generation of induced charges lead to the increased scattering rate from strong phonon anharmonicity. Thus, the ultra-low thermal conductivity emerges with external electric field applied. Applying external electric field to regulate thermal conductivity enlightens the constructive idea for high-efficient thermal management.

physics.comp-ph↗

arXiv2022

The stable behavior of low thermal conductivity in 1T-sandwich structure with different components

Designing materials with low thermal conductivity (\k{appa}) is of demand for thermal protection, heat insulation, thermoelectricity, etc. In this paper, based on the start-of-art first-principles calculations, we propose a framework of a 1T-sandwich structure for designing materials with low \k{appa}. The 1T-sandwich structure is the same as the well-known transition metal dichalcogenide (TMD) but with light Carbon atoms in the middle plane. Using different atoms to fill the outer positions, a few novel two-dimensional materials are constructed as study cases, i.e., Mg2C, Janus MgBeC, Be2C, and Mo2C. With a systematic and comparative study, the \k{appa} are calculated to be 3.74, 8.26, 14.80, 5.13 W/mK, respectively. The consistent values indicate the stable behavior of low \k{appa} in the 1T-sandwich structure, being insensitive to the component. Our study would help design advanced functional materials with reliable heat transfer performance for practical applications, which reduces the influence of unavoidable impurities.

cond-mat.mtrl-sci↗

arXiv2022

The synergistic modulation of electronic and geometry structures leads to ultra-low thermal conductivity of graphene-like borides (g-B3X5, X=N, P, As)

The design of novel devices with specific technical interests through modulating structural properties and bonding characteristics promotes the vigorous development of materials informatics. Herein, we propose a synergy strategy of component reconstruction by combining geometric configuration and bonding characteristics. With the synergy strategy, we designed a novel two-dimensional (2D) graphene-like borides, e.g. g-B3N5, which possesses counter-intuitive ultra-low thermal conductivity of 21.08 W/mK despite the small atomic mass. The ultra-low thermal conductivity is attributed to the synergy effect of electronics and geometry on thermal transport due to the combining reconstruction of g-BN and nitrogene. With the synergy effect, the dominant acoustic branches are strongly softened, and the scattering absorption and Umklapp process are simultaneously suppressed. Thus, the thermal conductivity is significantly lowered. To verify the component reconstruction strategy, we further constructed g-B3P5 and g-B3As5, and uncovered the ultra-low thermal conductivity of 2.50 and 1.85 W/mK, respectively. The synergy effect and the designed ultra-low thermal conductivity materials with lightweight atomic mass cater to the demand for light development of momentum machinery and heat protection, such as aerospace vehicles, high-speed rail, automobiles.

cond-mat.mtrl-sci↗

arXiv2022

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 {\mu}m. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems.

cond-mat.mtrl-sci↗

arXiv2003

Nearly Scale-Invariant Spectrum of Adiabatic Fluctuations May be from a Very Slowly Expanding Phase of the Universe

In this paper we construct an expanding phase with phantom matter, in which the scale factor expands very slowly but the Hubble parameter increases gradually, and assume that this expanding phase could be matched to our late observational cosmology by the proper mechanism. We obtain the nearly scale-invariant spectrum of adiabatic fluctuations in this scenario, different from the simplest inflation and usual ekpyrotic/cyclic scenario, the tilt of nearly scale-invariant spectrum in this scenario is blue. Although there exists an uncertainty surrounding the way in which the perturbations propagate through the transition in our scenario, which is dependent on the detail of possible "bounce" physics, compared with inflation and ekpyrotic/cyclic scenario, our work may provide another feasible cosmological scenario generating the nearly scale-invariant perturbation spectrum.

hep-th↗