arXiv subjects

Elvis F. Arguelles

Publications and source records attributed to Elvis F. Arguelles.

5 recordsLinked to original sources

arXiv2026

Open Quantum System Theory of Muon Spin Relaxation in Materials

We present a non-Markovian theory of muon spin relaxation that treats the implanted muon as an open quantum spin coupled to a temporally correlated local magnetic environment. Using a Schwinger-Keldysh influence-functional formulation, we derive a stochastic equation of motion for the muon spin, in which the fluctuation kernel is fixed by the local-field correlation tensor, while the retarded memory torque is introduced through an effective phenomenological backaction kernel. In the appropriate limits, the theory reduces to standard Kubo-Toyabe descriptions. This enables quantitative, global analysis of zero-field (ZF) and weak longitudinal-field (LF) $μ$SR spectra beyond the strong-collision approximation. Applied to $\mathrm{Li}_{0.73}\mathrm{CoO}_2$, the model supports a decomposition into a quenched width and a Li-driven dynamical component within the adopted parametrization, and yields fluctuation rates approximately consistent with activated behavior over the intermediate-temperature window. The fitted memory parameter is most visible in the crossover between quasi-static and fast-fluctuation limits.

cond-mat.mtrl-sci↗

arXiv2025

Dynamics with Simultaneous Dissipations to Fermionic and Bosonic Reservoirs

We introduce a non-phenomenological framework based on the influence functional method to incorporate simultaneous interactions of particles with fermionic and bosonic thermal reservoirs. In the slow-motion limit, the electronic friction kernel becomes Markovian, enabling an analytical expression for the friction coefficient. The framework is applied to a prototypical electrochemical system, where the metal electrode and solvent act as fermionic and bosonic reservoirs, respectively. We investigate quantum vibrational relaxation of hydrogen on metal surfaces, showing that dissipation to electron-hole pairs reduces the relaxation time. Additionally, in solvated proton discharge, electronic friction prolongs charge transfer by delaying proton transitions between potential wells. This study provides new insights into the interplay of solvent and electronic dissipation effects, with direct relevance to electrochemical processes and other systems involving multiple thermal reservoirs.

cond-mat.other↗

arXiv2024

Time-dependent electron transfer and energy dissipation in condensed media

We study a moving adsorbate interacting with a metal electrode immersed in a solvent using the time-dependent Newns-Anderson-Schmickler model Hamiltonian. We have adopted a semiclassical trajectory treatment of the adsorbate to discuss the electron and energy transfers that occur between the adsorbate and the electrode. Using the Keldysh Green's function scheme, we found a non-adiabatically suppressed electron transfer caused by the motion of the adsorbate and coupling with bath phonons that model the solvent. The energy is thus dissipated into electron-hole pair excitations, which are hindered by interacting with the solvent modes and facilitated by the applied electrode potential. The average energy transfer rate was discussed in terms of the electron friction coefficient and given an analytical expression in the slow-motion limit.

cond-mat.mes-hall↗

arXiv2022

Using neural network potential to study point defect properties in multiple charge states of GaN with nitrogen vacancy

Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant physical quantities, these calculations increase the computational loads substantially, which often limits the application of this method to large-scale systems. In this study, we propose a new scheme of neural network potential (NNP) to analyze the point defect behavior in multiple charge states. The proposed scheme necessitates only minimal modifications to the conventional scheme. We demonstrated the prediction performance of the proposed NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0, 1+, 2+, and 3+. The proposed scheme accurately trained the total energies and atomic forces for all the charge states. Furthermore, it fairly reproduced the phonon band structures and thermodynamics properties of the defective structures. Based on the results of this study, we expect that the proposed scheme can enable us to study more complicated defective systems and lead to breakthroughs in novel semiconductor applications.

cond-mat.mtrl-sci↗

arXiv2020

Phase stability of Au-Li binary systems studied using neural network potential

The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we constructed a neural network potential (NNP) of Au-Li binary systems based on density functional theory (DFT) calculations. To accelerate construction of NNPs, we proposed an efficient and inexpensive method of structural dataset generation. The predictions by the constructed NNP on lattice parameters and phonon properties agree well with those obtained by DFT calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with fine composition grids, showing excellent agreement with DFT verifications. We found the existence of various compositions with structures on and slightly above the convex hull, which can explain the lack of consensus on the Au-Li stable phases in previous studies. Moreover, we newly found Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported elsewhere. Finally, we examined the alloying process starting from the phase separated structure to the complete mixing phase. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. This paper demonstrates the applicability of NNPs toward miscible phases and provides the understanding of the alloying mechanism.

cond-mat.mtrl-sci↗