Related papers: Machine learning modeling of superconducting criti…
I propose a superconductivity model, which is based on the assumption that stripes in high-Tc cuprates (a) exist and (b) organize themselves in a two-dimensional superstructure. The model describes hole states, which are localized either…
The phenomenon of superconductivity occurs in the phase space of three principal parameters: temperature T, magnetic field B, and current density Jd . The critical temperature Tc is one of the first parameters that is measured and in a…
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…
Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure…
The density functional theory for superconductors developed in the preceding article [cond-mat/0408685] is applied to the calculation of superconducting properties of several elemental metals. In particular, we present results for the…
The problems of participation of crystal chemical factors in creating of conditions to occurrence of a superconducting state in layered superconductors are considered. On the basis of the experimental data, given in the literature, new…
Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen…
Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic…
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex…
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 critical temperature (${T}_\text{c}$) of superconductors varies a lot. The factors governing the ${T}_\text{c}$ may hold key clues to understand the nature of the superconductivity. Thereby, ${T}_\text{c}$-involved correlations, such as…
Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis…
Although there is no complete theory of high temperature superconductivity, the importance of CuO$_2$ planes in cuprate superconductors is confirmed from both theory and experiments. Strong Coulomb repulsion between electrons on the CuO$_2$…
Over the past decade, a combination of crystal structure prediction techniques and experimental synthetic work has thoroughly explored the phase diagrams of binary hydrides under pressure. The fruitfulness of this dual approach is…
Scaling laws express a systematic and universal simplicity among complex systems in nature. For example, such laws are of enormous significance in biology. Scaling relations are also important in the physical sciences. The seminal 1986…
For a given crystal structure, say body-centred-cubic, the many-body Hamiltonian in which nuclear and electron motions are to be treated from the outset on the same footing, has parameters, for the elements, which can be classified as (i)…
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…
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high…
The search for hydride compounds that exhibit high $T_c$ superconductivity has been extensively studied. Within the range of binary hydride compounds, the studies have been developed well including data-driven searches as a topic of…
The superconducting properties of a layered system are analyzed for the cases of zero- and non-zero angular momentum of the pairs. The effective thermodynamic potential for the quasi-2D XY-model for the gradients of the phase of the order…