Related papers: Critical Temperature Prediction for a Superconduct…
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…
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…
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
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…
Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine…
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces…
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…
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…
A model, based on classical mechanics and thermodynamics and valid for \textit{all} superconductors, is devised to investigate the properties of the \textit{current-driven}, superconducting to normal transition. This process is shown to be…
This paper demonstrates the method of estimation of critical temperature Tc value of high-temperature superconductors from the dispersive part of AC susceptibility measurement using a pair of neural networks.
Predicting the transition temperature, Tc, of a superconductor from Periodic Table normal state properties is regarded as one of the grand challenges of superconductivity. By studying the correlations of Periodic Table properties with known…
High-temperature superconductivity occurs in strongly correlated materials such as copper oxides and iron-based superconductors. Numerous experimental and theoretical works have been done to identify the key parameters that induce…
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge…
We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula. The statistical model gives reasonable out-of-sample predictions: $\pm 9.5$ K…
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…
A wide variety of superconducting oxides are used to test a general model of high pressure induced transition temperature (T c) changes. The T c 's vary from a low of 24 K to a high of 164 K. Although the model is capable of predicting both…
Superconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the initial endeavor to apply machine learning…
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore…
The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular "first principle" methods for…