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To date, density functional theory (DFT) is one of the most accurate and yet practical theory to gain insight about materials properties. Although successful, the computational cost is the main hurdle even today. A way out is combining DFT…

Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the…

The work presents experimental results of precision magnetoresistance dr(H,T) measurements obtained for canonical heavy fermion compounds CeAl2, CeAl3, CeCu6 and substitutional solid solutions CeCu6-xAux (x=0.1 and 0.2) and Ce(Al0.95M0.05)2…

We investigate the effect of Mn substitution in Fe$_2$Ti$_{1-x}$Mn$_x$Sn on electronic structure and magnetic and electrical transport properties. The spin-polarized density of states calculations using density-functional theory (DFT)…

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…

The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules with targeted properties. However, the focus has often been on creating highly efficient predictive models, sometimes at the…

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We demonstrate the importance of preserving physical…

Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation…

The discovery of complex concentrated alloys has unveiled materials with diverse atomic environments, prompting the exploration of solute segregation beyond dilute alloys. Data-driven methods offer promising for modeling segregation in such…

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors…

Density is one of the most commonly measured or estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science and sustainable cements.…

A new methodology for micromagnetic simulations of magnetic nanocomposites is presented. The methodology is especially suitable for simulations of two-phase composites consisting of magnetically hard inclusions in a soft magnetic matrix…

The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and…

First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…

Using the density functional theory, we perform a full atomic relaxation of the bulk ferrite with 12.5%-concentration of monoatomic interstitial Cr periodically located at the edges of the bcc Fe$_\alpha$ cell. We show that structural…

Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face…

Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…

We report electrical resistivity measurements and neutron diffraction studies under magnetic fields of Bi$_{1.75}$Pb$_{0.35}$Sr$_{1.90}$Cu$_{0.91}$Fe$_{0.09}$O$_{6+y}$, in which hole carriers are overdoped. This compound shows short-range…

In this paper, we present a theoretical formulation of magnetization dynamics in disordered binary alloys based on Kubo linear response theory interfaced with the combination of seamlessly three approaches; density functional based…

Magnetite has attracted increasing attention in recent years due to its promising and diverse applications in biomedicine. Theoretical modelling can play an important role in understanding magnetite-based nanomaterials at the atomic scale…