arXiv subjects

Leandro Liborio

Publications and source records attributed to Leandro Liborio.

5 recordsLinked to original sources

arXiv2026

Hidden Magnetic Complexity Within a Simple van der Waals Ferromagnet Ce$_2$Te$_5$

Ce$_2$Te$_5$ is a layered $f$-electron van der Waals magnet in which reduced dimensionality and inequivalent Ce sites give rise to competing magnetic interactions. We investigate its magnetic ground state using muon spin relaxation ($μ$SR), neutron powder diffraction (NPD), and inelastic neutron scattering (INS). Zero-field $μ$SR reveals an onset of static magnetism below $T_{\mathrm{C}} = 5.0(1)$~K, followed by an additional anomaly in the internal field at $T_{\mathrm{2}} = 2.3(2)$~K, consistent with features observed in bulk thermodynamic and transport measurements. In contrast, NPD data collected between $0.05-8$~K reveal a single long-range ordered magnetic phase below $T_{\mathrm C}$, with the magnetic Bragg intensities vanishing at $T_{\mathrm C}$ with no evidence for additional structural or magnetic phase transitions down to base temperature. The ordered state is characterized by a commensurate propagation vector $\mathbf{k}=(0,0,0)$ and ferromagnetic alignment of Ce moments along the crystallographic $b$ axis. Remarkably, only one of the two crystallographically distinct Ce sites carries an ordered moment of $\sim 0.85(3)~μ_{\mathrm B}$ per Ce at 0.05~K. INS measurements establish the crystal electric field (CEF) energy scale of Ce$^{3+}$, revealing low-lying excitations at 7.94 and 21.46~meV and a Kramers doublet ground state with strong single-ion anisotropy. These results demonstrate that Ce$_2$Te$_5$ undergoes a single symmetry-breaking magnetic transition, while additional low-temperature anomalies reflect subtle modifications of the ordered state driven by competing interactions and CEF effects.

cond-mat.str-el↗

arXiv2026

MuDirac 1.3.0: A Sustainable Software Tool for Calculating Ground State Nuclear Properties Using Muonic X-Ray Measurements

The nuclear charge radius is one of the most fundamental quantities of the atomic nucleus. It can be deduced from a combination of experimental measurements of muonicX-raytransitionenergieswithmodellingofthoseX-raytransitionenergies. In thisworkwepresentMuDirac (1.3.0), whichisanopen, publiclyavailable, sustainable and computationally efficient software tool that will be at put the disposal of the negative muon community. With MuDirac (1.3.0), the community will be able to accurately and efficiently estimate nuclear properties, such as the nuclear charge radius, by assuming a 2-parameter Fermi distribution of the nuclear charge.

physics.comp-ph↗

arXiv2020

Computational Prediction of Muon Stopping Sites: a Novel Take on the Unperturbed Electrostatic Potential Method

Finding the stopping site of the muon in a muon-spin relaxation experiment is one of the main problems of muon spectroscopy, and computational techniques that make use of quantum chemistry simulations can be of great help when looking for this stopping site. The most thorough approach would require the use of simulations - such as Density Functional Theory (DFT)- to test and optimise multiple possible sites, accounting for the effect that the added muon has on its surroundings. However, this can be computationally expensive and sometimes unnecessary. Hence, in this work we present a software implementation of the Unperturbed Electrostatic Method (UEP), which is an approach used for finding the muon stopping site in crystalline materials that calculates the minima of the electrostatic potential in the crystalline material, and estimates the stopping site of the muon relying on the approximation that the muon presence does not significantly affect its surroundings. One of the main assumptions of the UEP is that the muon stopping site will be one of the electrostatic potential minima in the crystalline material. In this regard, we also propose some symmetry-based considerations about the properties of the electrostatic potential of the crystalline material. In particular, which sites are more likely to be its minima and why the unperturbed approximation may be sufficiently robust for them. We introduce the Python software package pymuon-suite and the various utilities it provides to facilitate these calculations, and finally, we demonstrate the effectiveness of the method with some chosen example systems.

cond-mat.mtrl-sci↗

arXiv2018

Comparison between Density Functional Theory and Density Functional Tight Binding approaches for finding the muon stopping site in organic molecular crystals

Finding the possible stopping sites for muons inside a crystalline sample is a key problem of muon spectroscopy. In a previous work, we suggested a computational approach to this problem, using Density Functional Theory software in combination with a random structure searching approach using a Poisson sphere distribution. In this work we test this methodology further by applying it to three organic molecular crystals model systems: durene, bithiophene, and tetracyanoquinodimethane (TCNQ). Using the same sets of random structures we compare the performance of Density Functional Theory software CASTEP and the much faster lower level approximation of Density Functional Tight Binding provided by DFTB+, combined with the use of the 3ob-3-1 parameter set. We show the benefits and limitations of such an approach and we propose the use of DFTB+ as a viable alternative to more cumbersome simulations for routine site-finding in organic materials. Finally, we introduce the Muon Spectroscopy Computational Project software suite, a library of Python tools meant to make these methods standardized and easy to use.

physics.comp-ph↗

arXiv2018

Computational Prediction of Muon Stopping Sites Using Ab Initio Random Structure Searching (AIRSS)

The stopping site of the muon in a muon-spin relaxation experiment (μ+SR) is in general unknown. There are some techniques that can be used to guess the muon stopping site, but they often rely on approximations and are not generally applicable to all cases. In this work, we propose a purely theoretical method to predict muon stopping sites in crystalline materials from first principles. The method is based on a combination of ab initio calculations, random structure searching and machine learning, and it has successfully predicted the MuT and MuBC stopping sites of muonium in Si, Diamond and Ge, as well as the muonium stopping site in LiF, without any recourse to experimental results. The method makes use of Soprano, a Python library developed to aid ab-initio computational crystallography, that was publicly released and contains all the software tools necessary to reproduce our analysis.

cond-mat.mtrl-sci↗