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Machine Learning Model

We introduce here the theoretical framework for Machine Learning (ML) 1, a statistical learning method which can be used to create models based on the available data. ML models enable prediction of materials properties, for example.

Parameters

The list of parameters affecting ML is presented in this page.

Properties

We discuss the classification of properties in the context of ML in a separate section of the documentation.

Units

We introduce some Machine Learning-specific unit types under this explanation.

Structured Representation

This page contains an example structured representation for the ML model.

Actions

We introduce here the action for uploading a training data set to our platform via the Subworkflow Editor Interface.

Example Workflow

We review in this page the structure of an example Machine Learning workflow.