https://github.com/wardlt/ward-npj-2016-examples
Scripts for replicating a paper on predicting the properties of materials with machine learning
https://github.com/wardlt/ward-npj-2016-examples
Last synced: 10 months ago
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Scripts for replicating a paper on predicting the properties of materials with machine learning
- Host: GitHub
- URL: https://github.com/wardlt/ward-npj-2016-examples
- Owner: WardLT
- Created: 2017-11-03T14:24:59.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-11-03T14:28:41.000Z (over 8 years ago)
- Last Synced: 2025-04-13T22:47:46.775Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 28.6 MB
- Stars: 3
- Watchers: 0
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Predicting Properties of Inorganic Materials with Machine Learning
This repository contains software and scripts necessary to replicate most calculations from a 2016 paper by [Ward *et al*](https://www.nature.com/articles/npjcompumats201628): "A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials."
The scripts are all contained within Jupyter notebooks alongside explainations for the calculations.
## Contents
There are several important directories in this repository:
`datasets`: Datasets for the band gap energy predictions and glass forming ability models
`magpie`: The Materials-Agnostic Platform for Informatics and Exploration (Magpie), its required libraries, and documentation. See [bitbucket.org](https://bitbucket.org/wolverton/magpie)
`predicting-band-gap-energies`: Scripts for creating models for band gap energies of crystalline compounds
`modeling-metallic-glasses`: Scripts for predicting the glass-forming ability of metallic alloys
The latter two directories contain Jupyter notebooks that replicate the key tables and figures from this paper.
## Running
These notebooks are designed to be run via Docker.
Docker is a tool for creating very lightweight virtual machines, which - in our case - makes it possible to run these notebooks in the same software environment.
To launch the notebooks, first install docker on your computer and then call either `./docker.bs` if you are running Mac or Linux, or double-click `docker.bat` if you are running Windows.
This will create a Docker container with the correct environment, assign it in an appropriate amount of RAM (though you might want to adjust it, if your computer has <6GB of RAM), and allow it to access the appropriate files.
Once the docker container is launched, you can connect to the Jupyter environment via a web browser (see the URL listed by the Docker container).
Then, you can either run each notebook on its own, or run `./run-all.bs inplace` in the `/home/joyvan/data` directory via the command line to execute all notebooks in the proper order.