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The primary phase of the Earth's lower mantle, (Al, Fe)-bearing bridgmanite, transitions to the postperovskite (PPv) phase at Earth's deep mantle conditions. Despite extensive experimental and ab initio investigations, there are still…
High-entropy pyrochlore oxides possess ultra-low thermal conductivity and excellent high-temperature phase stability, making them promising candidate for next-generation thermal barrier coating (TBC) materials. However, reliable predictive…
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic…
Various physical properties (electronic, optical and thermoelectric) of cubic perovskite oxides $SrXO_3\; (X=Sc,\; Ge,\; Si)$ are investigated by using the density functional theory (DFT) within Wien2k code. This code is based on different…
Thermoelectric materials with a high figure of merit (ZT) are highly demanded for a sustainable solution to the energy crisis. In this study, we have predicted three new double perovskite oxides (DPOs), Ba2GaXO6 (X = V, Nb, Ta), with high…
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces (PESs) very accurately at a fraction of computational cost.…
Wide bandgap semiconductors (WBGs) are predicted to be the potential materials for energy generation and storing. In this work, we used density functional theory (DFT) that incorporates generalized gradient approximation (GGA) and…
Which density functional is the "best" for structure simulations of a particular material? A concise, first principles, approach to answer this question is presented. The random phase approximation (RPA)--- an accurate many body theory---…
By applying density functional theory (DFT) approximations, we present a first-principles investigation of elastic properties for the experimentally verified phases of a metallic perovskite LaNiO$_{3}$. In order to improve the accuracy of…
We combine density functional theory (DFT) within the local density approximation (LDA), the quasiharmonic approximation (QHA), and a model vibrational density of states (VDoS) to calculate elastic moduli and sound velocities of…
Dynamic density functional theory (DDFT) is a promising approach for predicting the structural evolution of a drying suspension containing one or more types of colloidal particles. The assumed free-energy functional is a key component of…
Elemental rare-earth metals provide a playground for studying novel electron correlation effects and complex magnetism. However, ab initio simulations of these systems remain challenging. Here, we employ fully charge self-consistent density…
Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Machine learning (ML) and artificial intelligence (AI) address…
Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is…
Determining the finite temperature structure of the hybrid perovskite MAPbI3 is a challenge for both experimental and theoretical methods. A very powerful computational method that can resolve the atomic structure is molecular dynamics…
Materials with low work functions are critical for an array of applications requiring the facile removal or efficient transport of electrons through a device. Perovskite oxides are a promising class of materials for finding low work…
The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high…
In recent years, metal halide perovskites (MHPs) for optoelectronic applications have attracted the attention of the scientific community due to their outstanding performance. The fundamental understanding of their physicochemical features…
We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total…
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a…