Functional form of the superconducting critical temperature from machine learning

  title={Functional form of the superconducting critical temperature from machine learning},
  author={Stephen R. Xie and Gregory R. Stewart and James J. Hamlin and P. J. Hirschfeld and Richard G. Hennig},
  journal={Physical Review B},
Predicting the critical temperature $T_c$ of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors for which the theory is relatively well understood. Early attempts by McMillan and Allen and Dynes to improve on the weak-coupling BCS formula led to closed-form approximate relations between $T_c$ and various measures of the phonon spectrum and the electron-phonon interaction appearing in Eliashberg theory. Here we propose that these approaches can… Expand

Figures from this paper

In search for near-room-temperature superconducting critical temperature of metal superhydrides under high pressure: A review
An overview and the latest status of the superconductivity of metal superhydrides under high pressure are discussed in this review article. The searching for near-room-temperature superconductorsExpand
Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing
This work identifies an analytical equation that correctly predicts the octahedral tilting classification for 49 perovskite oxides in a dataset of 60 materials and finds that the equation outperforms the other models as well as the original tolerance factor in predicting octahed tilting. Expand
High Temperature Superconductors
  • M. Ikram, A. Raza, +7 authors J. Haider
  • Materials Science
  • Transition Metal Compounds - Synthesis, Properties, and Application
  • 2021
One of the pioneers who introduced superconductivity of metal solids was Kamerlingh Onnes (1911). Researchers always struggled to make observations towards superconductivity at high temperatures forExpand
Recent advances in high-throughput superconductivity research
The history of high-throughput research paradigm is briefly reviewed, some recent applications of this paradigm in superconductivity research are focused on, and the role these methods can play in all stages of materials development, including high- throughput computation, synthesis, characterization and the emerging field of machine learning for materials is considered. Expand
Machine learning in materials science: From explainable predictions to autonomous design
Abstract The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences,Expand
Towards high-throughput superconductor discovery via machine learning
Towards high-throughput superconductor discovery via machine learning S. R. Xie,1,2 Y. Quan,1,2 Ajinkya Hire,1,2 Laura Fanfarillo,3,4 G. R. Stewart,3 J. J. Hamlin,3 R. G. Hennig,1,2 and P. J.Expand
Symmetric Helmholtz Fermi-surface harmonics for an optimal representation of anisotropic quantities on the Fermi surface: Application to the electron-phonon problem
We outline a numerical procedure to incorporate the crystal symmetries in the Helmholtz Fermi-surface harmonics basis set, which are the solutions of the Helmholtz equation defined on the FermiExpand
High-throughput investigation of the formation of double spinels
Spinel compounds, with the general chemical formula AB2O4, are a wide class of materials, where A and B can be a variety of cations, providing this structure with a great deal of functionalExpand
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
A critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design are reviewed. Expand
Artificial intelligence for search and discovery of quantum materials
Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantumExpand


Machine learning modeling of superconducting critical temperature
A team led by Valentin Stanev from the University of Maryland at College Park and including researchers from Duke University and NIST develops several machine learning schemes to model the critical temperature (Tc) of over 12,000 known superconductors and candidate materials. Expand
Ab Initio Approach and Its Impact on Superconductivity
One of the main motivations for studying superconductivity is to search for high-temperature superconductors, especially room-temperature superconductors. During the long history of more than 100Expand
Conventional superconductivity at 190 K at high pressures
The highest critical temperature of superconductivity Tc has been achieved in cuprates: 133 K at ambient pressure and 164 K at high pressures. As the nature of superconductivity in these materials isExpand
Superconductivity at 250 K in lanthanum hydride under high pressures
A lanthanum hydride compound at a pressure of around 170 gigapascals is found to exhibit superconductivity with a critical temperature of 250 kelvin, the highest critical temperature that has been confirmed so far in a superconducting material. Expand
Electron-phonon coupling and exchange-correlation effects in superconducting H3S under high pressure
We investigate the ${\rm H_3S}$ phase of sulphur hydride under high pressure $\simeq$ 200 GPa by means of {\it ab-initio} calculations within the framework of the density-functional theory (DFT) withExpand
Effect of pressure on superconducting Ca-intercalated graphiteCaC6
The pressure effect on the superconducting transition temperature ($T_c$) of the newly-discovered Ca-intercalated graphite compound CaC$_6$ has been investigated up to $\sim$ 16 kbar. $T_c$ is foundExpand
Potential high-Tc superconducting lanthanum and yttrium hydrides at high pressure
The study suggests that dense hydrides consisting of lanthanum and yttrium and related hydrogen polyhedral networks may represent new classes of potential very high-temperature superconductors. Expand
Evidence for Superconductivity above 260 K in Lanthanum Superhydride at Megabar Pressures.
It is suggested that the transitions represent signatures of superconductivity to near room temperature in phases of lanthanum superhydride, in good agreement with density functional structure search and BCS theory calculations. Expand
Lattice dynamics of NbX2(X = S,Se) is studied by taking account of electron-phonon interaction derived microscopically on the basis of the realistic tight-binding bands fitted to the first-principleExpand
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. DespiteExpand