DOI: 10.5061/dryad.866t1g20b
title: Data from: High-throughput computational screening of bioinspired dual atom alloys for CO2 activation

description: This is a large DFT dataset on screening materials (dual atom alloys in Cu
 111 surface) for aptitude for binding CO and CO2, as well as their
 relative formation energy and relevant machine learning features.
description: This dataset was collected with atomic relaxations using density
 functional theory and processed with machine learning algorithms from
 SKLearn.
description: # Data for "High-throughput computational screening of bioinspired
 dual atom alloys for CO2 activation"
 [https://doi.org/10.5061/dryad.866t1g20b](https://doi.org/10.5061/dryad.866t1g20b) ## Description of the data and file structure A high throughput screening study for catalytic binding energies and thermodynamics was performed by altering atom cites in the surface of Cu 111 to be different transition metals. ### File: catMLfinal.tar Subfolders contain their own more detailed READMEs ### /plots All of the primary metadata is contained in the ./plots directory, which contains all the information used for plots in the original manuscript. In all CSV files; * Dopants is the atom types inserted into the DAA * xxx BE(eV) is the calculated binding energy for the xxx species onto the surface * Elemental FE(eV) is the calculated formation energy for the dual atom alloy species /BE-FEscatter contains raw data for the initial screening of all 400 SAAs and DAAs * sorted by early/mid/late transition metals and row of periodic table /ML contains data used in Fig.3 which is the machine learning feature selection and plot of specific values * featureselection.py does the ML feature selection and plots feature importance * can tune what features and methods are used * correlationmatrix.py does Pearson Correlation plot * crossvalidate.py does n-fold cross validation plots for each ML method * plotallscatter and plotscatter and plot do basic scatter plots using input csv files /BE-COscatter is the Fig 4 data with CO binding and CO2 binding * Lines and colors in the final plot were added with post processing /accurate includes the comparison between gamma point and denser k-point grid plot in the SI Each directory contains it's own README (except pseudos, tests, and manuscripts) #### /COLE ./COLE contains adsorbate DFT relaxation calculations and code from the collaborator Cole Clark #### /database ./database contains all of the possible DAA combinations of two different dopants each sub directory in ./database*/dopants/ (must unzip) contains all raw DFT relaxation calculations for that DAA #### ./databasehomo contains DAA of homodoping two atoms #### ./databasesingle contains SAA for each element and pure Cu #### ./databaseCO contains all DAA and SAA from the second step of screening #### ./scripts contains generation and workup scripts #### ./pseudos contains the pseudopotentials that were used for this project \- norm conserving, srl, generated by opium ## Code/software Python version 3 and Quantum Espresso Version 7 are required to reproduce results. Only python is required for viewing plots. ## Access information Data was derived from the following sources: * [https://doi.org/10.1021/jacs.2c13253](https://doi.org/10.1021/jacs.2c13253)