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Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics i...
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... -
Raman spectra of 2D titanium carbide MXene from machine-learning force field ...
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... -
Ranking the synthesizability of hypothetical zeolites with the sorting hat
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided... -
eQM7: a dataset for small molecules with Foster-Boys centers
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... -
Machine learning of superconducting critical temperature from Eliashberg theory
The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and... -
Accurate and scalable multi-element graph neural network force field and mole...
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... -
Understanding the diversity of the metal-organic framework ecosystem
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... -
Simulating solvation and acidity in complex mixtures with first-principles ac...
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... -
Global free-energy landscapes as a smoothly joined collection of local maps
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... -
Ranking the synthesizability of hypothetical zeolites with the sorting hat
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided... -
Teaching ML in Compact Courses
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... -
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep...
Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication... -
Data publication: Bubble size distribution and electrode coverage at porous n...
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... -
TEAM – The Transformer Earthquake Alerting Model
TEAM, the Transformer Earthquake Alerting Model is a deep learning model for real time estimation of peak ground acceleration (TEAM), earthquake magnitude and earthquake... -
Fast earthquake assessment and earthquake early warning dataset for Italy
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.... -
Fast earthquake assessment dataset for Chile
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... -
Model files for the Neural network-based model of Electron density in the Top...
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...
