77 datasets found

<p>Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of...

<p>Liquid electrolytes adsorbed at the surface of metallic electrodes display a multitude of structures that can largely differ from the parent bulk system, both in terms...

This data contains: - the molecular direct correlation functions (DCF) of bulk CO2 in four different supercritical conditions (rho = 0.4, 0.7, 0.8 and 2.0 rho_c and T = 1.2...

Data and scripts for paper "Modelling Membrane Reshaping by Staged Polymerization of ESCRT-III Filaments". Including data points for figures in main and SI, and LAMMPS input file.

This dataset includes the numerical results discussed in the paper entitled "Elastic modelling of lattice distortions in concentrated random alloys" published in Acta Materialia...

A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural...

Obtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The...

Obtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The...

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio...

Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor...

Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor...

Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor...

Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic...

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and...

In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory...

One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the...

This record contains files necessary to reproduce enhanced sampling well-tempered metadynamics (wtMTD) and parallel tempering metadynamics in the well-tempered ensemble...

<p>Lithium thiophosphate (LPS) has demonstrated promising properties for use as a solid electrolyte for the next-generation of lithium ion batteries. However, the high...

Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere,...

<p><span lang="EN-US">Thermal transport in low-dimensional materials is of great fundamental and applied interest due to their unusual and widely tunable properties,...