arXiv subjects

Chenxu Yu

Publications and source records attributed to Chenxu Yu.

3 recordsLinked to original sources

arXiv2026

Hidden ordered compound-layer and its tailoring of the electronic/optical property in Ge2Sb2SexTe5-x alloys

Ge2Sb2SexTe5-x (GSST) alloys represent an emerging class of phase-change materials for integrated photonics. However, the microscopic origins underlying their superior performance compared to the parent compound Ge2Sb2Te5 remain elusive. By using atomic simulations, this work elucidates that the thermal stability and low optical loss of GSST are fundamentally governed by the formation of an in-layer compound-like structure with SeTe2 or Se2Te stoichiometry depending on the Se content, contrasting to the previously believed pure-element-layered model where Se and Te atoms occupy separate layers inside GSST. The newly identified compound-layered structures maintaining stability at temperature above 370 K, yield an enlarged bandgap, weakened antibonding character, and more importantly, a moderate refractive index as well as decreased extinction coefficient which align better with the experiment compared to the previously believed model. The present findings not only help bridge the long-standing theory-experiment gap regarding the optical properties of GSST by redefining its atomic structure, but also establish local chemical ordering as a critical materials design principle for high-performance photonics.

cond-mat.mtrl-sci↗

arXiv2026

Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method

As a representative continuous-depth neural network approach, stochastic differential equation (SDE)-based Bayesian neural networks (BNNs) have attracted considerable attention due to their solid theoretical foundations and strong potential for real-world applications. However, their reliance on numerical SDE solvers inevitably incurs a large number of function evaluations (NFEs), resulting in high computational cost and occasional convergence instability. To address these challenges, we propose a Nesterov-accelerated gradient (NAG) enhanced SDE-BNN model. By integrating NAG into the SDE-BNN framework along with an NFE-dependent residual skip connection, our method accelerates convergence and substantially reduces NFEs during both training and testing. Extensive empirical results show that our model consistently outperforms conventional SDE-BNNs across various tasks, including image classification and sequence modeling, achieving lower NFEs and improved predictive accuracy.

stat.ML↗

arXiv2023

A method of maximum likelihood fit to data with non-uniform efficiencies

Estimations of physical parameters using data usually involve non-uniform experimental efficiencies. In this article, a method of maximum likelihood fit is introduced using the efficiency as a weight, while the probability distribution function is kept unaffected by the efficiency. A brief proof and pseudo-experiment studies suggest that this method gives unbiased estimation of parameters. For cases where the probability distribution function can be normalized analytically, this method significant reduces the usage of computing resources.

physics.data-an↗