SARA_ScienceAdvances

The raw data is available from eCommons (https://doi.org/10.7298/h63q-9r54). Download the image/spectroscopy data separately, and to process the data follow the instructions below.

Preprocessing

Before running below scripts, copy the raw data from microscopy imaging and reflectance measurements available from https://doi.org/10.7298/h63q-9r54 to ProcessingData/Bi2O3, or modify the paths in the relevant python files.

Extract features from images

In ProcessingData, using a python version later than 3.6, execute

python get_gp-bias.py

All relevant package dependencies can be satisfied through standard PyPI package installs. This script will extract the features from the images and create separate plots, which contain the image itself as well as the RGB and LSA bias. The bias features will be written to a file called bias.json.

Process and plot reflectance spectroscopy

In ProcessingData, execute

python get_legcoeff.py

This script will read and normalize the reflectance data, and expand the spectra in Legendre coefficients. The coefficients will be written to a file called legendre_coefficients.json.

Active learning set up

Process the legendre_coefficients.json and bias.json files using the inner_data_organizer.jl script in SARA.jl/ScienceAdvances2021/inner/. Make sure to adjust the path variable in the script to match the location of the .json files. Using a version of Julia later than 1.6.2, execute the install.jl file in GaussianDistributions.jl and SARA.jl, in that order. If the add operation of GaussianDistributions.jl for SARA.jl fails, add the package locally by typing ]add path/to/GaussianDistributions.jl\ in the REPL. After that, everything is set up for the active learning benchmarks.

Active learning benchmarks for characterization loop

In the SARA.jl/ScienceAdvances2021/inner/ directory, there are the inner_kernel_benchmark.jl and inner_acquisition_benchmark.jl files which can be used to reproduce the results reported in Figure 2 of the main article. inner_kernel_plot.jl and inner_acquisition_plot.jl can further create the plots used in the figure.

Active learning benchmarks for synthesis loop

In the SARA.jl/ScienceAdvances2021/outer/ directory, there are the outer_kernel_benchmark.jl and outer_acquisition_benchmark.jl files which can be used to reproduce benchmark results for the synthesis loop, the acquisition benchmark being reported in Figure 3 of the main article. outer_kernel_plot.jl and outer_acquisition_plot.jl can further create the plots for the benchmarks. outer_gradient_map.jl records the gradient maps for varying kernel lengthscale parameters and stripe sampling techniques, used to explore the behavior of the results with respect to changes in these hyper-parameters. outer_gradient_learning.jl executes active learning and records the evolution of the gradient maps and outer_gradient_plot.jl plots the corresponding results as shown in Figure 4 of the main article.

References

Software written by Maximilian Amsler and Sebastian Ament, released on 7/24/2021.

If using this work for a publication, please cite: "Autonomous synthesis of metastable materials", Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover, arXiv:2101.07385 (will be replaced with the link to the published article).

Disclaimer

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 THE DEVELOPER 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.