ATOMICA: Learning Universal Representations of Intermolecular Interactions
Code for automated fitting of machine learned interatomic potentials.
Chemical intuition for surface science in a package.
Modulated automation of cluster expansion based on atomate2 and Jobflow
This repository contains the LAMMPS and python scripts created from the ground-up, along with the most important data, to conduct a thorough analysis of the Thermal Rectification (TR) in semi-stochastically generated atomistic models of polycrystalline graphene with graded grain size variation - using Molecular Dynamics & mapping of phonon modes.
Code and experiments accompanying our paper Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties at NeurIPS 2022
ASD2VTK is a Python tool that enables the conversion of output data from UppASD simulations to VTK files for easy visualization and post-processing in Paraview.
Domain ontology for atomistic and electronic modelling
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