arXiv subjects

Menghan Chen

Publications and source records attributed to Menghan Chen.

5 recordsLinked to original sources

arXiv2026

First-principles study of photovoltaic and thermoelectric properties of AgBiSCl2

This work systematically investigates the potential of the hybrid anion semiconductor AgBiSCl2 for photovoltaic and thermoelectric applications, aiming to provide theoretical guidance for high-performance energy conversion devices. Structural analysis reveals favorable ductility and a relatively low Debye temperature. Analysis of interatomic interactions indicates that Ag-S and Ag-Cl bonds are relatively weak, resulting in local structural softness and enhanced lattice anharmonicity. These weak bonds facilitate phonon scattering and give rise to low-frequency localized rattling vibrations primarily associated with Ag atoms, contributing to reduced lattice thermal conductivity. In contrast, Bi-S bonds exhibit stronger, directional interactions, which help stabilize the overall structure. The coexistence of weak bonding and strong lattice coupling enables favorable modulation of thermal transport properties.Optically, AgBiSCl2 possesses a high static dielectric constant and exhibits strong absorption in the ultraviolet region. In terms of thermal transport, phonon spectrum exhibit mode hardening with temperature increasing. The localized Ag vibrations intensify the anharmonicity, reducing phonon lifetimes and group velocities.For electronic transport, the p-type material maintains a higher Seebeck coefficient than the n-type, while the latter shows greater electrical conductivity. At 700 K, the figure of merit reaches 0.77 for p-type and 0.69 for n-type AgBiSCl2, indicating promising high-temperature thermoelectric performance.In summary, AgBiSCl2 exhibits excellent potential for dual photovoltaic and thermoelectric applications. Its unique bonding features and lattice response mechanisms offer valuable insights into designing multifunctional energy conversion materials.

cond-mat.mtrl-sci↗

arXiv2025

Designing LLM-simulated Immersive Spaces to Enhance Autistic Children's Social Affordances Understanding

One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.

cs.HC↗

arXiv2024

Irreversible charging caused by energy dissipation from depinning of droplets on polymer surfaces

Interfacial energy dissipation during stick-slip motion of a liquid drop on a non-conductive polymer substrate is shown to lead to an irreversible increase in electrical charge. This previously unobserved phenomenon occurs during surface wetting, in contrast to the previously reported charge separation mechanism that occurs during dewetting. Understanding this electrification mechanism will facilitate the design of energy harvesters and aid the development of risk mitigation strategies for electrostatic buildup in liquid flow across a wide range of industrial applications.

cond-mat.soft↗

arXiv2021

Quantum Gaussian process regression

In this paper, a quantum algorithm based on gaussian process regression model is proposed. The proposed quantum algorithm consists of three sub-algorithms. One is the first quantum subalgorithm to efficiently generate mean predictor. The improved HHL algorithm is proposed to obtain the sign of outcomes. Therefore, the terrible situation that results is ambiguous in terms of original HHL algorithm is avoided, which makes whole algorithm more clear and exact. The other is to product covariance predictor with same method. Thirdly, the squared exponential covariance matrices are prepared that annihilation operator and generation operator are simulated by the unitary linear decomposition Hamiltonian simulation and kernel function vectors is generated with blocking coding techniques on covariance matrices. In addition, it is shown that the proposed quantum gaussian process regression algorithm can achieve quadratic faster over the classical counterpart.

quant-ph↗

arXiv2021

Quantum Algorithms for Prediction Based on Ridge Regression

We propose a quantum algorithm based on ridge regression model, which get the optimal fitting parameters w and a regularization hyperparameter α by analysing the training dataset. The algorithm consists of two subalgorithms. One is generating predictive value for a new input, the way is to apply the phase estimation algorithm to the initial state |Xi and apply the controlled rotation to the eigenvalue register. The other is finding an optimal regularization hyperparameter α , the way is to apply the phase estimation algorithm to the initial state |yi and apply the controlled rotation to the eigenvalue register. The second subalgorithm can compute the whole training dataset in parallel that improve the efficiency. Compared with the classical ridge regression algorithm, our algorithm overcome multicollinearity and overfitting. Moreover, it have exponentially faster. What's more, our algorithm can deal with the non-sparse matrices in comparison to some existing quantum algorithms and have slightly speedup than the existing quantum counterpart. At present, the quantum algorithm has a wide range of application and the proposed algorithm can be used as a subroutine of other quantum algorithms.

quant-ph↗