Related papers: Predicting Lattice Phonon Vibrational Frequencies …

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here we…

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for…

We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional theory calculations. We show that the ground-state electron…

Most of the work on chatter detection is based on laboratory machining tests, thus without the constraints of noise, the variety of situations to be managed in the industry, and the uncertainties on the parameters (sensor position, tool…

Using a microscopic dipole lattice model including electronic polarization (EP) of ions and local field effects (LFEs) self-consistently, two sets of three equations are deduced for long wavelength in-plane and out-of-plane optical lattice…

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…

In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine. ADPs capture atomic vibrations, reflecting thermal and structural…

The diffusion of large databases collecting different kind of material properties from high-throughput density functional theory calculations has opened new paths in the study of materials science thanks to data mining and machine learning…

Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks…

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of…

In the present work we have performed an ab initio calculation of vibrational properties of CuTe2O5 by means of density functional theory method. One has compared calculated values with known experimental data on Raman and infrared…

We combine polarization-resolved low frequency Raman microscopy and dispersion-corrected density-functional calculations (DFT-D3) to study polymorph-dependent lattice vibrations in coronene, a model molecular system for nanographenes and…

[Fe(phen)2(NCS)2] is a prototype transition metal complex material, which undergoes a phase transition between low-spin (LS) and high-spin (HS) phases, induced by temperature, pressure or light. Vibrational modes play a key role for…

Graphene sheets with regular perforations, dubbed as antidot lattices, have theoretically been predicted to have a number of interesting properties. Their recent experimental realization with lattice constants below 100 nanometers stresses…

Neural network models have recently demonstrated impressive prediction performance in complex systems where chaos and unpredictability appear. In spite of the research efforts carried out on predicting future trajectories or improving their…

The lattice dynamics for NiCo, NiFe, NiFeCo, NiFeCoCr, and NiFeCoCrMn medium to high entropy alloy have been investigated using the DFT calculation. The phonon dispersions along three different symmetry directions are calculated by the…

We calculate the phonon-dispersion relations of several two-dimensional materials and diamond using the density-functional based tight-binding approach (DFTB). Our goal is to verify if this numerically efficient method provides sufficiently…

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction.…

The viscosity of the suspension consisting of fine particles dispersed in a Newtonian liquid diverges close to the jamming packing fraction. The contact microstructure in suspensions governs this macroscopic behavior in the vicinity of…

Understanding the fundamental processes of light-matter interaction is important for detection of explosives and other energetic materials, which are active in the infrared and terahertz (THz) region. We report a comprehensive study on…