68 datasets found

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states...

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...

Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous...

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training...

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.

The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical...

Neural network (NN) force fields can predict potential energy surfaces with high accuracy and speed compared to electronic structure methods typically used to generate their...

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet,...

We study the oxo-hexametallate Li7TaO6 with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of ∼0.29 eV and a...

The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning...

In this work we elaborate on recently discovered invariance principles, according to which transport coefficients are, to a large extent, independent of the microscopic...

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...

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we...

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...

Ice nucleation is a process of great relevance in physics, chemistry, technology, and environmental sciences; much theoretical effort has been devoted to its understanding, but...

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...

Trajectories and spin densities for the bulk hydrated electron at the MP2 level of theory. The data represent the first ab initio molecular dynamics study of the hydrated...

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

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...