arXiv subjects

S. V. Dordevic

Publications and source records attributed to S. V. Dordevic.

At least 19 recordsLinked to original sources

arXiv2026

Sparse Statistical Modeling in Condensed Matter Physics

In this work we explore the possibility of using sparse statistical modeling in condensed matter physics. The procedure is employed to two well known problems: elemental superconductors and heavy fermions, and was shown that in most cases performs better than other AI methods, such as machine or deep learning. More importantly, sparse modeling has two major advantages over other methods: the ability to deal with small data sets and in particular its interpretabilty. Namely, sparse modeling can provide insight into the calculation process and allow the users to give physical interpretation of their results. We argue that many other problems in condensed matter physics would benefit from these properties of sparse statistical modeling.

cond-mat.supr-con↗

arXiv2025

A universal scaling of condensation temperature in quantum fluids

The phenomena of superconductivity and superfluidity are believed to originate from the same underlying physics, namely the condensation of either bosons or pairs of fermions (Cooper pairs). In this work I complied and analyzed literature data for a number of quantum fluids and showed that indeed they all follow the same simple scaling law. The critical temperature for condensation T$_c$ is found to scale with the condensate coherence length $ξ$ and the effective mass of condensing particles m$^{\ast}$. The scaling plot includes members of most known classes of superconductors, as well as a number of superfluids and condensates, such as $^3$He, $^4$He, dilute Bose and Fermi gases, excitons, polaritons, neutron superfluid and proton superconductor in neutron stars, nuclear pairing, quark--antiquark condensate and Higgs condensate. The scaling plot spans more that 24 orders of magnitude of critical temperatures, albeit the scaling exponent is not the one predicted by theory. The plot might help the search for a QCD axion.

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arXiv2024

Searching for new heavy fermions with deep learning

Deep learning models were developed and implemented to aid the search for new heavy fermion compounds. For the purpose of these calculations a database of more than 200 heavy fermions was compiled from the literature. The deep learning networks trained on the database were then used for regression calculations, and predictions were made about the coherence temperature, Sommerfeld coefficient and carrier effective mass of potential new heavy fermions. Classification calculations were also performed in order to check whether predicted heavy fermions are superconducting and/or antiferromagnetic. Chemical composition was the only physical predictor used during the learning process. Suggestions were made for future improvements in terms of expanding the database, as well as for other artificial intelligence calculations.

cond-mat.str-el↗

arXiv2024

Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors

Effective computational search holds great potential for aiding the discovery of High-Temperature Superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches at predicting new superconductors within existing superconductor families but have yet to be able to generate completely new families of superconductors. We address this limitation by implementing conditioning -- a method to control the generation process -- for our generative model and develop SuperDiff, a Denoising Diffusion Probabilistic Model (DDPM) with Iterative Latent Variable Refinement (ILVR) conditioning for HTS discovery -- the first deep generative model for superconductor discovery with conditioning on reference compounds. With SuperDiff, by being able to control the generation process, we were able to computationally generate completely new families of hypothetical superconductors for the very first time. Given that SuperDiff also has relatively fast training and inference times, it has the potential to be a very powerful tool for accelerating the discovery of new superconductors and enhancing our understanding of them.

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arXiv2022

ScGAN: A Generative Adversarial Network to Predict Hypothetical Superconductors

Despite having been discovered more than three decades ago, High Temperature Superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a Generative Adversarial Network (GAN) to efficiently predict new superconductors. ScGAN was trained on compounds in OQMD and then transfer learned onto the SuperCon database or a subset of it. Once trained, the GAN was used to predict superconducting candidates, and approximately 70\% of them were determined to be superconducting by a classification model--a 23-fold increase in discovery rate compared to manual search methods. Furthermore, more than 99\% of predictions were novel materials, demonstrating that ScGAN was able to potentially predict completely new superconductors, including several promising HTS candidates. This project presents a novel, efficient way to search for new superconductors, which may be used in technological applications or provide insight into the unsolved problem of high temperature superconductivity.

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arXiv2022

Clustering Superconductors Using Unsupervised Machine Learning

In this work we used unsupervised machine learning methods in order to find possible clustering structures in superconducting materials data sets. We used the SuperCon database, as well as our own data sets complied from literature, in order to explore how machine learning algorithms groups superconductors. Both conventional clustering methods like k-means, hierarchical or Gaussian mixtures, as well as clustering methods based on artificial neural networks like self-organizing maps, were used. For dimensionality reduction and visualization t-SNE was found to be the best choice. Our results indicate that machine learning techniques can achieve, and in some cases exceed, human level performance. Calculations suggest that the clustering of superconducting materials works best when machine learning techniques are used in concert with human knowledge of superconductors. We also show that in order to resolve fine subcluster structure in the data, clustering of superconducting materials should be done in stages.

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arXiv2022

Superuid density in overdoped cuprates: thin films versus bulk samples

Recent study of overdoped La$_{2-x}$Sr$_x$CuO$_4$ cuprate superconductor thin films by Božović {\it et al.} has revealed several unexpected findings, most notably the violation of the BCS description which was believed to adequately describe overdoped cuprates. In particular, it was found that the superfluid density in La$_{2-x}$Sr$_x$CuO$_4$ films decreases on the overdoped side as a linear function of critical temperature T$_c$, which was taken as evidence for the violation of the Homes' law. We show explicitly that the law is indeed violated, and as the main reason for violation we find that the superfluid density in Božović's films is suppressed more strongly than in bulk samples. Based on the existing literature data, we show that the superfluid density in bulk cuprate samples does not decrease with doping, but instead tends to saturate on the overdoped side. The result is also supported by our recent measurement of a heavily overdoped bulk La$_{2-x}$Sr$_x$CuO$_4$ sample. Moreover, this saturation of superfluid density might not be limited to cuprates, as we find evidence for similar behavior in two pnictide superconductor families. We argue that quantum phase fluctuations play an important role in suppressing the superfluid density in thin films.

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arXiv2020

Predicting new superconductors and their critical temperatures using unsupervised machine learning

We used the superconductors in the SuperCon database to construct element vectors and then perform unsupervised learning of their critical temperatures (T$_c$). Only the chemical composition of superconductors was used in this procedure. No physical predictors (neither experimental nor computational) of any kind were used. We achieved the coefficient of determination R$^2$$\simeq$0.93, which is comparable and in some cases higher then similar estimates using other artificial intelligence techniques. Based on this machine learning model, we predicted several new superconductors with high critical temperatures. We also discuss the factors that limit the learning process and suggest possible ways to overcome them.

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arXiv2018

Observation of cyclotron antiresonance in the topological insulator Bi2Te3

We report on the experimental observation of a cyclotron antiresonance in a canonical 3D topological insulator Bi$_2$Te$_3$. Magneto-reflectance response of single crystal Bi$_2$Te$_3$ was studied in 18 Tesla magnetic field, and compared to other topological insulators studied before, the main spectral feature is inverted. We refer to it as an antiresonance. In order to describe this unconventional behavior we propose the idea of an imaginary cyclotron resonance frequency, which on the other hand indicates that the form of the Lorentz force that magnetic field exerts on charge carriers takes an unconventional form.

cond-mat.str-el↗

arXiv2016

Fano q-reversal in topological insulator Bi2Se3

We studied magneto-optical response of a canonical topological insulator Bi$_2$Se$_3$ with the goal of addressing a controversial issue of electron-phonon coupling. Magnetic-field induced modifications of reflectance are very pronounced in the infrared part of the spectrum, indicating strong electron-phonon coupling. This coupling causes an asymmetric line-shape of the 60 cm$^{-1}$ phonon mode, and is analyzed within the Fano formalism. The analysis reveals that the Fano asymmetry parameter (q) changes sign when the cyclotron resonance is degenerate with the phonon mode. To the best of our knowledge this is the first example of magnetic field driven q-reversal.

cond-mat.str-el↗

arXiv2014

The fate of quasiparticles in the superconducting state

Quasiparticle properties in the superconducting state are masked by the superfluid and are not directly accessible to infrared spectroscopy. We show how one can use a Kramers--Kronig transformation to separate the quasiparticle from superfluid response and extract intrinsic quasiparticle properties in the superconducting state. We also address the issue of a narrow quasiparticle peak observed in microwave measurements, and demonstrate how it can be combined with infrared measurements to obtain unified picture of electrodynamic properties of cuprate superconductors.

cond-mat.supr-con↗

arXiv2013

Do organic and other exotic superconductors fail universal scaling relations?

Universal scaling relations are of tremendous importance in science, as they reveal fundamental laws of nature. Several such scaling relations have recently been proposed for superconductors; however, they are not really universal in the sense that some important families of superconductors appear to fail the scaling relations, or obey the scaling with different scaling pre-factors. In particular, a large group of materials called organic (or molecular) superconductors are a notable example. Here, we show that such apparent violations are largely due to the fact that the required experimental parameters were collected on different samples, with different experimental techniques. When experimental data is taken on the same sample, using a single experimental technique, organic superconductors, as well as all other studied superconductors, do in fact follow universal scaling relations.

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arXiv2010

Towards a two-dimensional superconducting state of La$_{2-x}$Sr$_{x}$CuO$_{2}$ in a moderate external magnetic field

We report a novel aspect of the competition and coexistence between magnetism and superconductivity in the high-$T_{c}$ cuprate La$_{2-x}$Sr$_{x}$CuO$_{4}$ (La214). With a modest magnetic field applied $H \parallel c$-axis, we monitored the infrared signature of pair tunneling between the CuO$_2$ planes and discovered the complete suppression of interlayer coupling in a series of underdoped La214 single crystals. We find that the in-plane superconducting properties remain intact, in spite of enhanced magnetism in the planes.

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arXiv2010

Normal state charge dynamics of Fe1.06Te0.88S0.14 superconductor probed with infrared spectroscopy

We have used optical spectroscopy to probe the normal state electrodynamic response of Fe$_{1.06}$Te$_{0.88}$S$_{0.14}$, a member of the 11 family of iron-based superconductors with T$_c$= 8 K. Measurements have been conducted over a wide frequency range (50 - 50000 cm$^{-1}$) at selected temperatures between 10 and 300 K. At low temperatures the material behaves as an "incoherent metal": a Drude-like peak is absent from the optical conductivity, and all optical functions reveal that quasiparticles are not well defined down to the lowest measured temperature. We introduce "generalized spectral weight" analysis and use it to track temperature induced redistribution of spectral weight. Our results, combined with previous reports, indicate that the 11 family of iron-based superconductors might be different from other families.

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arXiv2009

Signatures of electron-boson coupling in half-metallic ferromagnet Mn$_5$Ge$_3$: study of electron self-energy $Σ(ω)$ obtained from infrared spectroscopy

We report results of our infrared and optical spectroscopy study of a half-metallic ferromagnet Mn$_5$Ge$_3$. This compound is currently being investigated as a potential injector of spin polarized currents into germanium. Infrared measurements have been performed over a broad frequency (50 - 50000 cm$^{-1}$) and temperature (10 - 300 K) range. From the complex optical conductivity $σ(ω)$ we extract the electron self-energy $Σ(ω)$. The calculation of $Σ(ω)$ is based on novel numerical algorithms for solution of systems of non-linear equations. The obtained self-energy provides a new insight into electron correlations in Mn$_5$Ge$_3$. In particular, it reveals that charge carriers may be coupled to bosonic modes, possibly of magnetic origin.

cond-mat.str-el↗

arXiv2007

The Missing Link: Magnetism and Superconductivity

The effect of magnetic moments on superconductivity has long been a controversial subject in condensed matter physics. While Matthias and collaborators experimentally demonstrated the destruction of superconductivity in La by the addition of magnetic moments (Gd), it has since been suggested that magnetic fluctuations are in fact responsible for the development of superconducting order in other systems. Currently this debate is focused on several families of unconventional superconductors including high-Tc cuprates, borocarbides as well as heavy fermion systems where magnetism and superconductivity are known to coexist. Here we report a novel aspect of competition and coexistence of these two competing orders in an interesting class of heavy fermion compounds, namely the 1-1-5 series: CeTIn5 where T=Co, Ir, or Rh. Our optical experiments indicate the existence of regions in momentum space where local moments remain unscreened. The extent of these regions in momentum space appears to control both the normal and superconducting state properties in the 1-1-5 family of heavy fermion (HF) superconductors.

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arXiv2007

Disparities in the Josephson vortex state electrodynamics of high-Tc cuprates

We report on far infrared measurements of interplane conductivity for underdoped single-crystal YBa2Cu3Oy in magnetic field and situate these new data within earlier work on two other high-Tc cuprate superconductors, La(2-x)SrxCuO4 and Bi2Sr2CaCu2O(8+d). The three systems have displayed apparently disparate electrodynamic responses in the Josephson vortex state formed when magnetic field H is applied parallel to the CuO2 planes. Specifically, there is discrepancy in the number and field dependence of longitudinal modes observed. We compare and contrast these findings with several models of the electrodynamics in the vortex state and suggest that most differences can be reconciled through considerations of the Josephson vortex lattice ground state as well as the c-axis and in-plane quasiparticle dissipations.

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arXiv2006

Infrared probe of the anomalous magnetotransport of highly oriented pyrolytic graphite in the extreme quantum limit

We present a systematic investigation of the magnetoreflectance of highly oriented pyrolytic graphite in magnetic field B up to 18 T . From these measurements, we report the determination of lifetimes tau associated with the lowest Landau levels in the quantum limit. We find a linear field dependence for inverse lifetime 1/tau(B) of the lowest Landau levels, which is consistent with the hypothesis of a three-dimensional (3D) to 1D crossover in an anisotropic 3D metal in the quantum limit. This enigmatic result uncovers the origin of the anomalous linear in-plane magnetoresistance observed both in bulk graphite and recently in mesoscopic graphite samples.

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