Related papers: Predicting the Curie temperature of ferromagnets u…
Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging…
Magnetic materials have a plethora of applications ranging from informatics to energy harvesting and conversion. However, such functionalities are limited by the magnetic ordering temperature. In this work, we performed machine learning on…
We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant…
Predicting the Curie temperature ($T_\mathrm{C}$) of magnetic materials is crucial for advancing applications in data storage, spintronics, and sensors. We present a machine learning (ML) framework to predict $T_{\mathrm{C}}$ using a…
Various techniques can be employed to determine the temperature of magnetic transformation, whether it be the Curie or Neel temperature. The standard procedure typically involves creating alloys with defined compositions and performing…
This study presents a machine learning approach to predict the Curie temperature in binary alloys, specifically focusing on the Fe-Pt, Fe-Ni, Fe-Pd, and Co-Pt compounds within a concentration range of 10 to 90 atomic percent. The optimal…
When exploring new magnetic materials, the effect of alloying plays a crucial role for numerous properties. By altering the alloy composition, it is possible to tailor, e.g., the Curie temperature ($T_\text{C}$). In this work, $T_\text{C}$…
The Curie temperature ($T_C$) of binary alloy compounds consisting of 3$d$ transition-metal and 4$f$ rare-earth elements is analyzed by a machine learning technique. We first demonstrate that nonlinear regression can accurately reproduce…
Prediction of the Curie temperature is of significant importance for the design of ferromagnetic materials. Even though the Curie temperature has been estimated using the Heisenberg model, magnetic exchange coupling parameters widely used…
Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K)…
Theoretical prediction of Curie temperature (TC) is of vital importance for designing the spintronic devices in two-dimensional (2D) ferromagnetic materials. Herein, based on the extensive investigation of Monte Carlo simulations, we…
We determine from first-principles the Curie temperature Tc for bulk Co in the hcp, fcc, bcc, and tetragonalized bct phases, for FeCo alloys, and for bcc and bct Fe. For bcc-Co, Tc=1420 K is predicted. This would be the highest Curie…
Accurately predicting the Curie temperature ($T_c$) of ferroelectrics from first principles remains a major challenge, as theoretical estimates often fall significantly below experimental values. In this work, we investigate the origin of…
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…
We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several…
The scientists have shown great interest in the search for alternative means to generate energy, which are not contaminants and generate significant damage to the environment. One of the quite viable possibilities for this is to consider…
We present a data set of calculated Curie temperatures for the main-phase compounds of rare-earth permanent magnets. We employ ab initio electronic structure calculations for the itinerant ferromagnetism and an effective spin model for the…
We perform high-throughput first-principles computations to search the high Curie temperature ($T_{\rm C}$) two-dimensional ferromagnetic (2DFM) materials. We identify 79 2DFM materials and calculate their $T_{\rm C}$, in which Co$_2$F$_2$…
We compare three first-principles methods of calculating the Curie temperature in two-dimensional (2D) ferromagnetic materials (FM), modeled using the Heisenberg model, and propose a simple formula for estimating the Curie temperature with…
We study correlation effects and temperature dependencies in the electronic structure of thin ferromagnetic local-moment films. In a first step the Kondo-lattice model is investigated as a candidate for a proper representation of…