Related papers: Structure prediction based on ab initio simulated …
We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about $100$ atoms in the…
A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by…
The computational pipelines of single-particle cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) include an early particle-picking stage, in which a micrograph or tomogram is scanned to extract candidate particles,…
In this study, the structural and magnetic properties of Nd-doped BiFeO$_3$ materials with the formula Bi$_{1-x}$Nd$_x$FeO$_3$ $(x = 0 - 0.2)$ were investigated. Samples were prepared using the sol-gel method and annealed at 823 K for 4…
Discovering the low-energy conformations of a molecule is of great interest to computational chemists, with applications in {\em in silico} materials design and drug discovery. In this paper, we propose a variable neighbourhood search…
We present an operational method to determine the 'locally preferred structure'' of model liquids, a notion often put forward to explain supercooling of a liquid and glass formation. The method relies on finding the global minimum in the…
By means of molecular-dynamics simulations, temperature driven diffusionless structural phase transitions in equi- and nearly equiatomic ordered nickel-titanium alloys were investigated. For this purpose, a model potential from the…
In the development of first-principles high-throughput searches for materials with desirable functional properties, there is a clear need for an efficient method to determine the ground state and low-energy alternative structures of…
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges…
Applying long wavelength periodic potentials on quantum materials has recently been demonstrated to be a promising pathway for engineering novel quantum phases of matter. Here, we utilize twisted bilayer boron nitride (BN) as a moir\'e…
We report the computational investigation of a series of ternary X$_4$Y$_2$Z and X$_5$Y$_2$Z$_2$ compounds with X={Mg, Ca, Sr, Ba}, Y={P, As, Sb, Bi}, and Z={S, Se, Te}. The compositions for these materials were predicted through a search…
We predict general trends for surface segregation in a binary metal cluster based on the difference between the atomic properties of the constituent elements. Considering the attractive and repulsive contributions of the cohesive energy of…
Most elemental metals under ambient conditions adopt simple structures such as BCC, FCC and HCP in specific groupings across the Periodic Table, and on compression, many of these elements undergo transitions to surprisingly complex…
An efficient method for calculating the electronic structure of systems that need a very fine sampling of the Brillouin zone is presented. The method is based on the variational optimization of a "single" (i.e. common to all points in the…
In this report, the evolution of the local atomic structure of the Zr50Nb50 melt was investigated by applying temperature (2600 to 300 K) and pressure (0 to 50 Gpa) using classical molecular dynamics simulations. To gain clear insight into…
A new method for direct evaluation of both crystalline structure, bulk modulus B_0, and bulk-modulus pressure derivative B'_0 of solid materials with complex crystal structures is presented. The explicit and exact results presented here…
The most favorable structure of the synthesized TiCoSb half-Heusler alloy is explored theoretically and experimentally, and the best structure for thermoelectric conversion is reported. Rietveld refinement of the X-ray diffraction data…
A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sampling from thermodynamic ensembles and hoping for an…
Generative machine learning models like the Restricted Boltzmann Machine (RBM) provide a practical approach for ansatz construction within the quantum computing framework. This work introduces a method that efficiently leverages RBM and…
The structures and magnetic properties of the Co-Zr-B alloys near the Co5Zr composition were studied using adaptive genetic algorithm and first-principles calculations to guide further experimental effort on optimizing their magnetic…