X-ray diffraction and photoelectrochemistry analysis for Materials Structure-Property Factorization

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#### Package requirements (tested version): - pandas (1.1.5) - numpy (1.19.5) - tensorflow (2.5.0) #### Ternary plot requirements: - ternary (1.0.8) - matplotlib (3.4.2) Development occurred in an Anaconda environment running Python 3.8.10 ### code & data description ##### `NMF_illdiff.ipynb` code for data munging, factorization, and visualization of results. #### source data ##### `base_plates&samples.pck` pandas dataframe containing DRNets phase mapping solution and nested dataframes with photoelectrochemical measurement data ##### `EweInterCV3_cathodic.pck` pandas dataframe containing potentials from the cathodic sweep of CV measurements ##### `IilldiffCV3_cathodic.pck` pandas dataframe containing photocurrents from the cathodic sweep of CV measurements ##### `select_phases_info.csv` pandas dataframe containing phase information for DRNets phase mapping solution #### results ##### `illdiff_basis_info.csv` table containing phase information for the 11 factored basis patterns ##### `illdiff_compositions.npy` array of [Bi, Cu, V] atomic fractions for 335 samples; shape (335, 3) ##### `illdiff_factored_basis.npy` array of factored basis patterns; shape (11, 15) ##### `illdiff_phase_concentrations.npy` array of phase concentrations for 335 samples; shape (335, 11) ##### `illdiff_predictions.npy` array of predicted photocurrents for 335 samples; shape (335, 15)

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