Related papers: Machine Learning Approach to Predict Curie Tempera…
In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model…
A series on nanocrystalline Fe100-xCrx alloys prepared by mechanical alloying was investigated with X-ray diffraction (XRD), scanning electron microscopy (SEM) and M\"ossbauer spectroscopy (MS) techniques. XRD and SEM were used to…
We propose a ferromagnetic/ferroelectric hybrid double quantum well structure, and present an investigation of the Curie temperature (Tc) modulation in this quantum structure. The combined effects of applied electric fields and spontaneous…
In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, an all-round framework is presented which…
We report the structure, magnetic property and spin polarization of CoFeMnGe equiatomic quaternary Heusler alloy. The alloy was found to exist in the L21 structure with considerable amount of DO3 disorder. Thermal analysis result indicated…
Many alloys made by Additive Manufacturing (AM) require careful design of post-heat treatment as an indispensable step of microstructure engineering to further enhance the performance. We developed a high-throughput approach by fabricating…
We consider two-band double exchange model and calculate the critical temperature in ferromagnetic regime (Curie temperature). The localized spins are represented in terms of the Schwinger-bosons, and two spin-singlet Fermion operators are…
Quaternary phases formed in copper alloys are investigated through a combination of quantum-mechanical and classical computer simulations and active machine learning. Focus is given on nickel, silicon, and chromium impurities in a copper…
The close-packed AB$_2$ structures called Laves phases constitute the largest group of intermetallic compounds. In this paper we computationally investigated the pseudo-binary Laves phase system Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$…
The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify…
A Magnetic Cluster Expansion (MCE) model for ternary face-centered cubic Fe-Ni-Cr alloys has been developed using DFT data spanning binary and ternary alloy configurations. Using this MCE model Hamiltonian, we perform Monte Carlo…
Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-initio calculations, which are only feasible for very simple…
Rational material design by elemental substitution is useful in tailoring materials to have desirable properties. Here we consider three non-equivalent substitutional series based on Co$_2$FeGe, viz; (Co$_{2-\alpha}$Mn$_\alpha$)FeGe,…
Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…
We present calculations of the exchange interactions and Curie temperatures in Cr-based pnictides and chalcogenides of the form CrX with X=As, Sb, S, Se and Te, and the mixed alloys CrAs$_{50}$X$_{50}$ with X=Sb, S, Se, and Te. The…
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…
We present a forward prediction material screening framework designed to discover Pd-Cu alloys with improved B2 phase stability, thereby unlocking simultaneous $H_2$ generation and utilization. First, we trained CatBoost models with…
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental…
We study the exchange interactions in half-metallic Heusler alloys using first-principles calculations in conjunction with the frozen-magnon approximation. The Curie temperature is estimated within both mean-field (MF) and…
Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local…