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The structural, electrical transport and magnetic properties of perovskite oxides La1-xTexMnO3 have been investigated and thus the magnetic phase diagram of La1-xTexMnO3 compounds as a function of temperature and the doping level x has been…
This work explores the unique character of strongly correlated systems, specifically Mott-insulators, in the context of battery electrode materials. The study investigates the correlation between the proposed chemical potential evolution…
Despite decades of experimental and theoretical efforts, the origin of metal-insulator transitions (MIT) in strongly-correlated materials is one of the main longstanding problems in condensed matter physics. An archetypal example is V2O3,…
We explore the scaling description for a two-dimensional metal-insulator transition (MIT) of electrons in silicon. Near the MIT, $\beta_{T}/p = (-1/p)d(\ln g)/d(\ln T)$ is universal (with $p$, a sample dependent exponent, determined…
We report here the completion of the electronic structure of the majority of the known stoichiometric inorganic compounds, as listed in the International Crystal Structure Data-base (ICSD). We make a detailed comparison of the electronic…
The temperature ($T$) dependent metal-insulator transition (MIT) in VO$_2$ is investigated using bulk sensitive hard x-ray ($\sim$ 8 keV) valence band, core level, and V 2$p-3d$ resonant photoemission spectroscopy (PES). The valence band…
We study the metal-insulator transition (MIT) in weakly coupled disordered planes on the basis of a Non-Linear Sigma Model (NL$\sigma $M). Using two different methods, a renormalization group (RG) approach and an auxiliary field method, we…
The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier ($E_m$) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both…
The goal of this paper is to highlight several issues which are most crucial for the understanding of the ``metal-insulator transition'' in two dimensions. We discuss some common problems in interpreting experimental results on high…
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are…
The nature of the Mott transition in the absence of any symmetry braking remains a matter of debate. We study the correlation-driven insulator-to-metal transition in the prototypical 3D Mott system GaTa4Se8, as a function of temperature and…
The underlying mechanism of the metal-to-insulator transition (MIT) in BaVS$_3$ is investigated, using dynamical mean-field theory in combination with density functional theory. It is shown that correlation effects are responsible for a…
Developing fast and accurate methods to discover intermetallic compounds is relevant for alloy design. While density-functional-theory (DFT)-based methods have accelerated design of binary and ternary alloys by providing rapid access to the…
We present a theory describing the mechanism for the two-dimensional (2D) metal-insulator transition (MIT) in absence of disorder. A two-band Hubbard model is introduced, describing vacancy-interstitial pair excitations within the Wigner…
In the present paper we propose a doubly orbitally degenerate narrow-band model with correlated hopping. The peculiarity of the model is taking into account the matrix element of electron-electron interaction which describes intersite…
In strongly correlated electronic systems, such as manganites, the global transport behavior depends sensitively on the spin ordering, whose alteration often requires a large external magnetic field. Here we show that the spin ordering in…
Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural…
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…
Electrons in correlated insulators are prevented from conducting by Coulomb repulsion between them. When an insulator-to-metal transition is induced in a correlated insulator by doping or heating, the resulting conducting state can be…
Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We…