Introduction¶
This document provides step-by-step tutorials of conducting and analyzing different MAST-ML runs. For this tutorial, we will be using the dataset example_data.xlsx in the tests/csv/ folder and input file example_input.conf in tests/conf/.
MAST-ML requires two files to run: The first is the text-based input file (.conf extension). This file contains all of the key settings for MAST-ML, for example, which models to fit and how to normalize your input feature matrix. The second file is the data file (.csv or .xlsx extension). This is the data file containing the input feature columns and values (X values) and the corresponding y data to fit models to. The data file may contain other columns that are dedicated to constructing groups of data for specific tests, or miscellaneous notes, which columns can be selectively left out so they are not used in the fitting. This will be discussed in more detail below.
Throughout this tutorial, we will be modifying the input file to add and remove different sections and values. For a complete and more in-depth discussion of the input file and its myriad settings, the reader is directed to the dedicated input file section:
The data contained in the example_data.csv file consist of a previously selected matrix of X features created from combinations of elemental properties, for example the average atomic radius of the elements in the material. The y data values used for fitting are listed in the “Scaled activation energy (eV)” column, and are DFT-calculated migration barriers of dilute solute diffusion, referenced to the host system. For example, the value of Ag solute diffusing through a Ag host is set to zero. The “Host element” and “Solute element” columns denote which species comprise the corresponding reduced migration barrier.