arXiv subjects

P. A. Sousa

Publications and source records attributed to P. A. Sousa.

2 recordsLinked to original sources

arXiv2026

First principles electric field gradients at A and B site cations across the NaRTiO4 Ruddlesden Popper series

The $n = 1$ Ruddlesden-Popper titanates, NaRTiO$_{4}$ (R = rare-earth), exhibit a structural behaviour where non-centrosymmetry is driven by cooperative oxygen octahedral rotations (OORs) rather than conventional second-order Jahn-Teller distortions. In this work, we present an \textit{ab-initio} investigation of the structural, electronic and hyperfine properties of the entire NaRTiO$_{4}$ series across the two disputed ground states, $Pbcm$ and $P\bar{4}2_1m$, and the high temperature $P4/nmm$ symmetries. Our results reveal an ionic-radius-dependent evolution from a tilt-dominated regime for small rare-earth ions to a distortion-dominated regime for larger cations, leading to an asymptotic regime in which the high-temperature phase becomes increasingly competitive with the ground-state structures as the ionic radius increases. In parallel, the electronic band gap follows a systematic evolution across the series, reflecting the underlying structural changes and the increasing dominance of octahedral distortions at larger ionic radii. The Electric Field Gradient (EFG) tensor reveals that, in the large-radius limit, all symmetries tend locally towards a similar environment. Away from this limit, the EFG tensor for different symmetries progressively diverges, providing a sensitive probe for phase transitions and revealing symmetry-specific fingerprints, particularly for the rare-earth and Ti sites. By establishing these EFG signatures, this work provides a roadmap for experimental techniques, such as Nuclear Magnetic Resonance (NMR) and Perturbed Angular Correlation (PAC), to resolve the ground-state symmetry of these structures.

cond-mat.mtrl-sci↗

arXiv2016

Combining complex networks and data mining: why and how

The increasing power of computer technology does not dispense with the need to extract meaningful in- formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

physics.soc-ph↗