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A tutorial demonstrating the use of machine learning for the classification of crystal structures in a molecular dynamics simulation.

License: Creative Commons Attribution 4.0 International

Jupyter Notebook 100.00%
machine-learning notebook jupyter molecular-crystals molecular-dynamics python3 scikit-learn

mlcrystals-tutorial's Introduction

Machine Learning with Molecular Crystals

DOI Binder

This is a set of notebooks that detail how machine learning can be used in the detection of 2D molecular crystals. This work stems from research I am conducting as part of my PhD, studying the crystal formation of these molecules.

To get started quickly click the badge below.

Binder

Environment Setup

These notebooks require a fairly extensive set of dependencies which can be installed with the command

conda env update

which will create the conda environment MLCrystals containing all the required packages.

Installation without conda is probably not possible, it can be downloaded from here. It is also highly likely to be impossible to install on Windows.

Running the Notebooks

To run the notebook you need to be in the MLCrystals environment

source activate MLCrystals-tutorial

from which you can launch the jupyter server

jupyter notebook

which will open up jupyter in your default web browser.

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mlcrystals-tutorial's Issues

Update sdanalysis version

The current version of the analysis toolkit 0.3.18 includes dependencies for running the simulations along with analysing them. To reduce the number of requirements and make it simpler to get started, update to the latest version of statdyn-analysis which is currently 0.4.6.

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