doctests will actually run the code-blocks and ensure that you get the expected output. This should prevent documentation from going out of date or having silly typos.
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
A standalone React.js/Redux based web application for the design and visualization of atomistic materials structures. Used within the Exabyte.io platform and can be deployed in standalone mode.
Materials Design in Javascript (made.js). A JavaScript (Node) library allowing for the creation and manipulation of material structures from atoms up on the web.
JSON schemas and examples representing structural data, characteristic properties, modeling workflows and related data about materials standardizing the diverse landscape of information
Example usage of Exabyte.io platform through its RESTful API: programmatically create materials and modeling workflows, execute simulations on the cloud, analyze data and build machine learning models
ExPrESS: Exabyte Property Extractor, Sourcer, Serializer. A python package allowing to extract and standardize materials data from native format for physics-based simulation engines.
This repository is dedicated to the improvement of ionic conductivity in doped LLZO solid-state electrolytes using machine learning models with simplistic descriptors.
doctests will actually run the code-blocks and ensure that you get the expected output. This should prevent documentation from going out of date or having silly typos.