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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.
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