Related papers: Structure prediction based on ab initio simulated …
An archetypical spin-glass metallic alloy, Cu0.83Mn0.17, is studied by means of an ab-initio based approach. First-principles calculations are employed to obtain effective chemical, strain-induced and magnetic exchange interactions, as well…
Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic…
By fitting to a database of ab-initio forces and energies, we can extract pair potentials for alloys, with a simple six-parameter analytic form including Friedel oscillations, which give a remarkably faithful account of many complex…
Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely…
Nanotubular materials inspired by crystalline diborides such as AlB_2 are proposed. The atomic structure, in particular the basic chemical question of where to put Al atoms in order to stabilize nanotubular Al-B systems, is investigated…
Using the minima hopping global geometry optimization method on the density functional potential energy surface we study medium size and large boron clusters. Even though for isolated medium size clusters the ground state is a cage like…
We propose an approach for exploiting machine learning to approximate electronic fields in crystalline solids subjected to deformation. Strain engineering is emerging as a widely used method for tuning the properties of materials, and this…
Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling…
We calculate ground-state properties of a many-quark system in the string-flip model using variational Monte Carlo methods. The many-body potential energy of the system is determined by finding the optimal grouping of quarks into hadrons.…
Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of…
The lowest-energy structure, distribution of isomers, and their molecular properties depend significantly on the geometry and temperature. The total energy computations under DFT methodology are typically carried out at zero temperature;…
A simple and robust method able to predict, with high accuracy, the optical properties of single and multi-layer nanostructures is presented. The method exploits a COMSOL Multiphysics simulation platform and it has been validated by three…
We use ab initio pseudopotential total energy calculations with a plane wave basis set to investigate the structural energetics of various phases of polymorphic NbN. Particular attention is given to its recently discovered superconducting…
Ground state properties of SiC, AlN, GaN and InN in the zinc-blende and wurtzite structures are determined using an ab initio scheme. For the self-consistent field part of the calculations, the Hartree-Fock program Crystal has been used.…
A multitude of observed boron-based materials have outstanding superconducting, mechanical, and refractory properties. Yet, the structure, the composition, and the very existence of some reported metal boride (M-B) compounds have been a…
Boron nitride (BN) has been explored these days because of its extraordinary optical, chemical and mechanical properties. BN is sensitive to its crystal structure that slight change in lattice parameters enormously change its properties.…
Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A…
We present an efficient method to find minimum energy structures using energy estimates from accurate quantum Monte Carlo calculations. This method involves a stochastic process formed from the stochastic energy estimates from Monte Carlo…
Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often…
Using the Car-Parrinello technique, ab initio molecular dynamics simulations are performed for liquid NaSn alloys in five different compositions (20, 40, 50, 57 and 80 % sodium). The obtained structure factors agree well with the data from…