README.md

screening_paper_supporting_information

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Jupyter notebook detailing the generation of most of the data and figures in this article, as well as CSV files for others to use.

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Links to tribological data, preparation routines, and random forest models

1. The tribological data for the chemically indentical monolayer films

   • https://github.com/PTC-CMC/terminal_group_screening
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2. The tribological data for the chemically dissimilar monolayer films

   • https://github.com/PTC-CMC/terminal_groups_mixed
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3. The tribological data for the chemically identical monolayer films used for additional validation

   • https://github.com/PTC-CMC/terminal_group_screening_accuracy_test
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4. The tribological data for the chemically dissimilar monolayer films used for additional validation

   • https://github.com/PTC-CMC/terminal_group_mixed_original_16_new_3
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5. The collection of initialization and analysis routines primarily focused on monolayer systems

   • https://github.com/PTC-CMC/atools
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6. The collection of routines related to structure-property examination and machine learning.

   • https://github.com/PTC-CMC/atools_ml
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7. The random forest models for predicting the coefficient of friction and adhesion for systems of two contacting functionalized monolayers (not python importable)

   • https://github.com/PTC-CMC/random_forest_tg
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8. The random forest models for predicting the coefficient of friction and adhesion for systems of two contacting functionalized monolayers (python importable)

   • https://github.com/PTC-CMC/random_forest_tg/tree/accuracy_test
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