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Register a new userHigh-throughput discovery of high-temperature conventional superconductors
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We survey the landscape of binary hydrides across the entire periodic table from 10 to 500 GPa using a crystal structure prediction method. Building a critical temperature ($T_c$) model, with inputs arising from density of states calculations and Gaspari-Gyorffy theory, allows us to predict which energetically competitive candidates are most promising for high-$T_c$ superconductivity. Implementing optimisations, which lead to an order of magnitude speed-up for electron-phonon calculations, then allows us to perform an unprecedented number of high-throughput calculations of $T_c$ based on these predictions and to refine the model in an iterative manner. Converged electron-phonon calculations are performed for 121 of the best candidates from the final model. From these, we identify 36 above-100 K dynamically stable superconductors. To the best of our knowledge, superconductivity has not been previously studied in 27 of these. Of the 36, 18 exhibit superconductivity above 200 K, including structures of NaH$_6$ (248-279 K) and CaH$_6$ (216-253 K) at the relatively low pressure of 100 GPa.
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Two hydrogen-rich materials, H$_3$S and LaH$_{10}$, synthesized at megabar pressures, have revolutionized the field of condensed matter physics providing the first glimpse to the solution of the hundred-year-old problem of room temperature superconductivity. The mechanism underlying superconductivity in these exceptional compounds is the conventional electron-phonon coupling. Here we describe recent advances in experimental techniques, superconductivity theory and first-principles computational methods which have made possible these discoveries. This work aims to provide an up-to-date compendium of the available results on superconducting hydrides and explain how the synergy of different methodologies led to extraordinary discoveries in the field. Besides, in an attempt to evidence empirical rules governing superconductivity in binary hydrides under pressure, we discuss general trends in the electronic structure and chemical bonding. The last part of the Review introduces possible strategies to optimize pressure and transition temperatures in conventional superconducting materials as well as future directions in theoretical, computational and experimental research.
Stability of numerous unexpected actinium hydrides was predicted via evolutionary algorithm USPEX. Electron-phonon interaction was investigated for the hydrogen-richest and most symmetric phases: R$overline{3}$m-$AcH_{10}$, I4/mmm-$AcH_{12}$ and P$overline{6}$m2-$AcH_{16}$. Predicted structures of actinium hydrides are consistent with all previously studied Ac-H phases and demonstrate phonon-mediated high-temperature superconductivity with Tc in the range 204-251 K for R$overline{3}$m-$AcH_{10}$ at 200 GPa and 199-241 K for P$overline{6}$m2-$AcH_{16}$ at 150 GPa which was estimated by directly solving of Eliashberg equation. Actinium belongs to the series of d1-elements (Sc-Y-La-Ac) that form high-Tc superconducting (HTSC) hydrides. Combining this observation with p0-HTSC hydrides ($MgH_{6}$ and $CaH_{6}$), we propose that p0- and d1-atoms with low-lying empty orbitals tend to form phonon-mediated HTSC metal polyhydrides.
The theory of symmetry indicators has enabled database searches for topological materials in normal conducting phases, which has led to several encyclopedic topological material databases. Here, based on recently developed symmetry indicators for superconductors, we report our comprehensive search for topological and nodal superconductors among nonmagnetic materials in Inorganic Crystal Structure Database. A myriad of topological superconductors with exotic boundary states are discovered. When materials are symmetry-enforced nodal superconductors, positions and shapes of the nodes are also identified. These data are aggregated at Database of Topological and Nodal Supercoductors. We also provide a subroutine Topological Supercon, which allows users to examine the topological nature in the superconducting phase of any material themselves by uploading the result of first-principles calculations as an input. Our database and subroutine, when combined with experiments, will help us understand the unconventional pairing mechanism and facilitate realizations of the long-sought Majorana fermions promising for topological quantum computations.
In conventional metals, electron-phonon coupling, or the phonon-mediated interaction between electrons, has long been known to be the pairing interaction responsible for the superconductivity. The strength of this interaction essentially determines the superconducting transition temperature TC. One manifestation of electron-phonon coupling is a mass renormalization of the electronic dispersion at the energy scale associated with the phonons. This renormalization is directly observable in photoemission experiments. In contrast, there remains little consensus on the pairing mechanism in cuprate high temperature superconductors. The recent observation of similar renormalization effects in cuprates has raised the hope that the mechanism of high temperature superconductivity may finally be resolved. The focus has been on the low energy renormalization and associated kink in the dispersion at around 50 meV. However at that energy scale, there are multiple candidates including phonon branches, structure in the spin-fluctuation spectrum, and the superconducting gap itself, making the unique identification of the excitation responsible for the kink difficult. Here we show that the low-energy renormalization at ~50 meV is only a small component of the total renormalization, the majority of which occurs at an order of magnitude higher energy (~350 meV). This high energy kink poses a new challenge for the physics of the cuprates. Its role in superconductivity and relation to the low-energy kink remains to be determined.
Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a single material or small subset of materials, and discoveries are made more or less serendipitously. Recent increases in computing power, novel machine learning algorithms, and improved experimental capabilities offer new opportunities to revolutionize superconductor discovery. These will enable the rapid prediction of structures and properties of novel materials in an automated, high-throughput fashion and the efficient experimental testing of these predictions. Here, we review efforts to use machine learning to attain this goal.
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