Code and data associated with the publication "Machine-learning improves understanding of glass formation in metallic systems".
https://doi.org/10.1039/D2DD00026A
The code in this repository utilises a number of other packages to process data, train neural networks, evaluate those networks, and visualise predictions.
To run this code, execute the following:
git clone https://github.com/Robert-Forrest/GFA
cd GFA
python3 -m pip install -r requirements.txt
python3 __main__.py examples/simple.yaml
The examples directory contains configuration files for a number of situations.
simple.yaml contains configuration for the simple task, which
trains a standard neural-network model. The prediction targets are
defined in the targets list.
kfolds.yaml contains configuration for the kfolds task, which
performs k-folds cross-validation on the standard neural-network
model.
kfoldsEnsemble.yaml contains configuration for the kfoldsEnsemble
task, which performs ensembling to create a meta-learner based on the
submodels produced during k-folds cross-validation.
permutation.yaml contains configuration for the
feature_permutation task, which shuffles features and measures the
resulting change in model efficacy to judge their importance.
composition_scan.yaml contains configuration for the
composition_scan task, which takes as input alloy spaces such as
CuZr or FeNiBe, and creates graphs of features and predictions across
composition space.