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To run the CAMEO simulation code, run the script: FeGaPd_ALBO_200801a.m
All data can be found in the folder "data".
Function dependencies can be found in the folders "Graph Cut mex2.0" and "shared".

% This software was developed by employees of the National Institute of
% Standards and Technology (NIST), an agency of the Federal Government and
% is being made available as a public service. Pursuant to title 17 United
% States Code Section 105, works of NIST employees are not subject to
% copyright protection in the United States.  This software may be subject
% to foreign copyright.  Permission in the United States and in foreign
% countries, to the extent that NIST may hold copyright, to use, copy,
% modify, create derivative works, and distribute this software and its
% documentation without fee is hereby granted on a non-exclusive basis,
% provided that this notice and disclaimer of warranty appears in all
% copies.

% THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT ANY WARRANTY OF ANY KIND, EITHER
% EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY
% WARRANTY THAT THE SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED
% WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
% FREEDOM FROM INFRINGEMENT, AND ANY WARRANTY THAT THE DOCUMENTATION WILL
% CONFORM TO THE SOFTWARE, OR ANY WARRANTY THAT THE SOFTWARE WILL BE ERROR
% FREE.  IN NO EVENT SHALL NIST BE LIABLE FOR ANY DAMAGES, INCLUDING, BUT
% NOT LIMITED TO, DIRECT, INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES,
% ARISING OUT OF, RESULTING FROM, OR IN ANY WAY CONNECTED WITH THIS
% SOFTWARE, WHETHER OR NOT BASED UPON WARRANTY, CONTRACT, TORT, OR
% OTHERWISE, WHETHER OR NOT INJURY WAS SUSTAINED BY PERSONS OR PROPERTY OR
% OTHERWISE, AND WHETHER OR NOT LOSS WAS SUSTAINED FROM, OR AROSE OUT OF
% THE RESULTS OF, OR USE OF, THE SOFTWARE OR SERVICES PROVIDED HEREUNDER.

% A. Gilad Kusne, NIST, aaron.kusne@nist.gov, Release 8/01/2020
% If using this work for a publication, please cite:
% Kusne, A. Gilad, et al. "On-the-fly closed-loop materials discovery
% via Bayesian active learning." Nature communications 11.1 (2020): 1-11.