Researcher profile

Mouyang Cheng

Mouyang Cheng contributes to research discovery and scholarly infrastructure.

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Published work

3 published item(s)

preprint2026arXiv

Frustrated Magnetism in FeGe$_3$O$_4$ with a Chiral Trillium Network

The discovery of new magnetic ground states in geometrically frustrated lattices remains a central challenge in materials science. Here, we report the synthesis, structural characterization, and frustrated magnetic properties of FeGe$_3$O$_4$, a newly identified compound that crystallizes in the noncentrosymmetric cubic space group $P2_13$. In this structure, Fe atoms form an intricate double-trillium lattice with nearest-neighbor Fe--Fe distances of $\sim$4.2~Å, while Ge$^{2+}$ ions mediate magnetic interactions through Fe-Ge-Fe pathways. Field-dependent magnetization at 2~K shows a pronounced nonlinearity, reaching a maximum moment of 2.55(3)~$μ_\mathrm{B}$/Fe$^{2+}$ at 70~kOe without evidence of saturation. Magnetic susceptibility, heat capacity, and neutron scattering collectively reveal the onset of short-range magnetic interactions near 5~K, with no long-range ordering detected down to 0.06~K. Specific heat measurements demonstrate strong frustration: only $\sim$34\% of the expected magnetic entropy is recovered at 2.4~K. Taken together, these results establish FeGe$_3$O$_4$ as a rare example of a geometrically frustrated trillium-lattice magnet, offering a promising platform for exploring exotic quantum magnetic phenomena.

preprint2026arXiv

Integral Variable Range Hopping for Modeling Electrical Transport in Disordered Systems

The variable range hopping (VRH) model has been widely applied to describe electrical transport in disordered systems, providing theoretical formulas to fit temperature-dependent electric conductivity. These models rely on oversimplified assumptions that restrict their applicability and result in problematic fitting behaviors, yet their overusing situation is becoming increasingly serious. In this work we formulate an integral variable range hopping (IVRH) model, which replaces the empirical temperature power-law dependence in standard VRH theories with a physics-inspired integral formulation. The model builds upon the standard hopping probability $ω(R)$ w.r.t. hopping distance $R$ and incorporates the density of accessible electronic states through an effective volume function $V(R)$, which reflects the influence of system geometry. The IVRH formulation inherently reproduces both the Mott behavior at low temperatures and the Arrhenius behavior at high temperatures, respectively, and enables a smooth transition between the two regimes. We apply the IVRH model to two-dimensional, three-dimensional, and multi-layered systems. Monte Carlo simulations validate the model's predictions and yield consistent values for the fitting parameters, with substantially reduced variances compared to fitting using the standard VRH model. Furthermore, the improved robustness of IVRH also extends to the transport measurements in monolayer MoS$_2$ system and monolayer WS$_2$ system, enabling more physically meaningful interpretation.IVRH model offers a more stable and physically sound framework for interpreting hopping transport in low-dimensional amorphous materials, providing deeper insights into the universal geometric scaling factors that govern charge transport in disordered systems.

preprint2026arXiv

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium motion has been limited. Here we establish X-ray photon correlation spectroscopy (XPCS), combined with domain-adaptive machine learning, as a quantitative probe of GB dynamics. Temperature- and grain-size-dependent two-time XPCS measurements in nanocrystalline silicon reveal pronounced departures from time-translation invariance, showing that GB relaxation can remain far from equilibrium over experimental timescales. However, direct extraction of quantitative physical information from these high-dimensional, noisy fluctuation maps faces a significant challenge. To overcome this barrier, we develop a semi-supervised learning framework that transfers physical parameter labels from continuum simulations to unlabeled experimental XPCS maps through domain-adaptive representation alignment. This AI-augmented approach enables the extraction of key kinetic parameters, including bulk diffusivity, GB stiffness, and effective GB concentration, directly from experimental XPCS measurements. Our results show how machine learning can transform indirect fluctuation signals into quantitative materials dynamics, providing a general route to study non-equilibrium defect motion in solids.