1,158 datasets found

We reassess the modeling of amorphous silica bilayers as a two-dimensional classical system whose particles interact with an effective pairwise potential. We show that it is...

Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or...

Identification of the hydrogen lattice location in crystals is key to understanding and controlling hydrogen-induced properties. Combining nuclear reaction analysis with the ion...

Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes...

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks....

Identification of the hydrogen lattice location in crystals is key to understanding and controlling hydrogen-induced properties. Combining nuclear reaction analysis with the ion...

This dataset contains the results of density functional theory (DFT) calculations performed using Quantum ESPRESSO to study surface binding energies (SBE) and the formation...

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data...

Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately...

The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and...

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have...

In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed...

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have...

The dataset provides records of tensile properties of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on mechanical...

Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs...

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents...

The performance of Ni-based oxygen evolution reaction (OER) electrocatalysts is enhanced upon Fe incorporation into the structure or Fe uptake from the electrolyte. In light of...

In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed...

2D semiconductors are considered as a promising alternative to silicon for future electronics. This class of materials possesses different advantages including atomically sharp...

Raman spectra play an important role in characterizing two-dimensional materials, as they provide a direct link between the atomic structure and the spectral features. In this...