MAST-ML version 3.x¶
New changes to MAST-ML¶
As of MAST-ML version update 3.x and going forward, there are some significant changes to MAST-ML for users to be aware of:
MAST-ML major updates:
MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi.
Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github.
An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques.
MAST-ML minor updates:
More straightforward implementation of left-out test data, both designated manually by the user and via nested cross validation.
Improved integration of feature generation schemes in complimentary materials informatics packages, particularly matminer.
Improved data import schema based on locally-stored files, and via downloading data hosted on databases including Figshare, matminer, Materials Data Facility, and Foundry.
Support for generalized ensemble models with user-specified choice of model type to use as the weak learner, including support for ensembles of Keras-based neural networks.