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atomistic.net's Projects

2024-aupd-dft-data icon 2024-aupd-dft-data

Data for manuscript "In Situ Analysis of Gas Dependent Redistribution Kinetics in Bimetallic Au-Pd Nanoparticles"

aenet icon aenet

Atomic interaction potentials based on artificial neural networks

aenet-lammps icon aenet-lammps

Interface aenet with the LAMMPS molecular dynamics software (https://lammps.sandia.gov)

aenet-lammps-1 icon aenet-lammps-1

This package provides the interface module between aenet [1] and LAMMPS [2], patch of aenet for the LAMMPS library, and Artificial Neural network (ANN) potential parameter file of BCC iron.

aenet-pytorch icon aenet-pytorch

ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training

aenet-tinker icon aenet-tinker

Interface aenet with the Tinker molecular dynamics software

aetoms icon aetoms

Basic crystal structure visualization in Jupyter notebooks

dribble icon dribble

Lattice Monte-Carlo Percolation Simulations

gibbsml icon gibbsml

Prediction of reaction free energies with machine learning

howru icon howru

HowRU is a framework for the optimization of Hubbard U parameters for DFT+U calculations

melting icon melting

Machine-learning models for melting-temperature prediction

mlp-beginners-guide icon mlp-beginners-guide

Strategies for the Construction of Neural-Network Based Machine-Learning Potentials (MLPs)

mlse2020-workshop icon mlse2020-workshop

Materials from the ænet workshop at the MLSE 2020 meeting (https://www.eventbrite.com/e/tutorial-workshop-machine-learning-in-materials-science-tickets-128297271593)

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

tools-and-data icon tools-and-data

A collection of tools and databases for atomistic machine learning

xas-tools icon xas-tools

Tools related to X-ray absorption spectroscopy (XAS)

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