atomisticnet/aiqu-kickoff-bayer-berlin-2022

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atomisticnet/aiqu-kickoff-bayer-berlin-2022

atomisticnet

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Kick-off workshop ai.qu. Bayer Berlin

aenet

Atomic interaction potentials based on artificial neural networks

tools-and-data

A collection of tools and databases for atomistic machine learning

MLP-beginners-guide

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

Jupyter Notebook

ml-catalysis

Machine Learning for Catalysis

Jupyter Notebook

xas-tools

Tools related to X-ray absorption spectroscopy (XAS)

aenet-lammps

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

Jupyter Notebook

gibbsml

Prediction of reaction free energies with machine learning

Jupyter Notebook

psi-k-mlip-workshop-2021

Psi-k Machine-learning interatomic potential workshop 2021

Jupyter Notebook

aenet-PyTorch

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

aevo

Atomistic Evolution

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)

dribble

Lattice Monte-Carlo Percolation Simulations

aenet-python

Python interface for aenet

aenet-tinker

Interface aenet with the Tinker molecular dynamics software

Jupyter Notebook

MeLting

Machine-learning models for melting-temperature prediction

Jupyter Notebook

howru

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