308 datasets found

Here, we present model files and example scripts for the Neural network-based model of Electron density in the Topside ionosphere (NET). The model is based on radio occultation...

The data publication contains a dataset for fast assessment of earthquakes based on seismic waveforms. The dataset encompasses Northern Chile. Due to the large scale of the...

The data publication contains a dataset for fast assessment of earthquakes and early warning based on seismic waveforms. The dataset encompasses Italy and surrounding refions....

TEAM, the Transformer Earthquake Alerting Model is a deep learning model for real time estimation of peak ground acceleration (TEAM), earthquake magnitude and earthquake...

Porous materials are frequently used as e.g. electrodes or porous transport layers in various types of electrolyzers. A better understanding of the bubble dynamics on porous...

Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication...

This talk summarizes the experiences made with teaching Machine Learning within compact events that stretch over several days to a week maximum. Both speakers explain pitfalls...

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided...

This repository contains the scripts that were used to run the calculations that present a new biasing technique, the Adaptive Topography of Landscape for Accelerated Sampling...

Set of inputs to perform the calculations reported in the paper. The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate...

By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are...

Data includes the the ab initio molecular dynamic simulation of Li7P3S11 that was used to measure the performance of the GNNFF. The data is divided into training and testing...

The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and...

The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small...

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided...

MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman...

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...

Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large...

Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate...

Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically...