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We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of…

In this paper, we employ evolutionary algorithm along with the full-potential density functional theory (DFT) computations to perform a comprehensive search for the stable structures of stoichiometric (WS2)n nano-clusters (n=1-9), within…

Context Mixed-metal clusters have long been studied because of their peculiar properties and how they change with cluster size, composition and charge state and their potential roles in catalysis. The characterization of these clusters is…

Bimetallic nanoparticles are often superior candidates for a wide range of technological and biomedical applications, thanks to their enhanced catalytic, optical, and magnetic properties, which are often better than their monometallic…

We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal…

Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional…

New Nd-Fe-B crystal structures can be formed via the elemental substitution of LATX host structures, including lanthanides LA, transition metals T, and light elements X as B, C, N, and O. The 5967 samples of ternary LATX materials that are…

Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly describe bond breaking and making energetics. Commonly used…

Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a…

The integration of density functional theory (DFT) with machine learning enables efficient \textit{ab initio} electronic structure calculations for ultra-large systems. In this work, we develop a transfer learning framework tailored for…

The formation energies of nanostructures play an important role in determining their properties, including the catalytic activity. For the case of 15 different rutile and 8 different perovskite metal oxides, we find that the density…

Density functional theory calculations have been performed on Indium nanoclusters (Inn, n= 3 to 10) to explore the relative stability among their different isomers and interaction with H2. Geometry optimizations starting from initial…

By means of density functional theory (DFT) and the generalized gradient approximation (GGA) we present a structural, electronic and magnetic study of FePt, CoPt, FeAu and FePd based L1$_0$ ordered cuboctahedral nanoparticles, with total…

Bulk Au-Fe alloys separate into Au-based fcc and Fe-based bcc phases, but L1$_0$ and L1$_2$ orderings were reported in single-phase Au-Fe nanoparticles. Motivated by these observations, we study the structural and ordering energetics in…

Using spin polarized density functional theory (DFT) based calculations, combined with ab-initio molecular dynamics simulation, we carry out a systematic investigation of the bimetallic Ni$_{13-n}$Ag$_n$ nano clusters, for all compositions.…

The structure, dynamics and stability of Fe-Pt nanoparticles have been investigated using DFT-based techniques: total energy calculations and DFT molecular dynamics. The investigated systems included multi-shell and disordered nanoparticles…

We present a comprehensive density functional theory (DFT) study of Mo-doped silver clusters Ag$_n$Mo ($n=1$-14), focusing on their structural, electronic, and bonding properties. Global optimization reveals an evolution from planar and…

Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density…

Density functional theory (DFT) is used to study vibrations, electrical dipole moments, and polarizabilities of NaF clusters. Because of prior experimental and theoretical studies, this is a good model system for tracking the evolution of…

The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour…