Related papers: Predicting the Curie temperature of ferromagnets u…
Gd6FeBi2 is reported as the only one room-temperature magnet with a Curie temperature (Tc) of ca. 350 K among more than hundreds of compounds with its structural type, which makes it more attractive in potential applications. To reveal the…
The prospects of half-metallic ferromagnetism being induced by the incorporation of C atoms into alkaline-earth-metal-oxides are investigated by the first principle calculation. The origin of the ferromagnetism is discussed through the…
Atomic scale simulations at finite temperature are an ideal approach to study the thermodynamic properties of magnetic transition metals. However, the development of interatomic potentials explicitly taking into account magnetic variables…
Mean-field-theory predicts that the Curie temperature T_c of a (III,Mn)V ferromagnet will be proportional to the valence band density-of-states of its host (III,V) semiconductor, suggesting a route toward room-temperature ferromagnetism in…
We theoretically investigate impurity correlation and magnetic clustering effects on the long-range ferromagnetic ordering in diluted magnetic semiconductors, such as $\textrm{Ga}_{1-x}\textrm{Mn}_{x}\textrm{As}$, using analytical arguments…
Prediction of critical temperature $(T_c)$ of a superconductor remains a significant challenge in condensed matter physics. While the BCS theory explains superconductivity in conventional superconductors, there is no framework to predict…
The Hubbard model on fcc-type lattices is studied in the dynamical mean-field theory of infinite spatial dimensions. At intermediate interaction strength finite temperature Quantum Monte Carlo calculations yield a second order phase…
We consider two-band double exchange model and calculate the critical temperature in ferromagnetic regime (Curie temperature). The localized spins are represented in terms of the Schwinger-bosons, and two spin-singlet Fermion operators are…
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…
Determining the angular momentum compensation temperature of ferrimagnets is an important step towards ferrimagnetic spintronics, but is not generally easy to achieve it experimentally. We propose a way to estimate the angular momentum…
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…
We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to…
Electronic structure calculations are used to examine the magnetic properties of Fe$_2$P-based alloys and the mechanisms through which the Curie temperature and magnetocrystalline anisotropy can be optimized for specific applications. It is…
Employing first-principles electronic structure calculations in conjunction with the frozen-magnon method we study the effective exchange interactions and spin waves in local moment ferromagnets. As prototypes we have chosen three…
The pressure dependence of electronic structure, exchange interactions and Curie temperature in ferromagnetic Heusler alloy Ni2MnSn has been studied theoretically within the framework of the density-functional theory. The calculation of the…
Seeking to unravel the enigmatic Verwey transition and its interplay with magnetism, we have conducted comprehensive measurements on the temperature-dependent electrical resistivity and magnetic moment of stoichiometric and doped-magnetite…
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random…
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To…
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding…
Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation…