Data for Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops
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2023-07-01
2024-03-01
2024-03-01
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2024-11-25
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Title
Data for Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops
Published Date
2024-12-02
Author Contact
Noordhoek, Kyle
noord014@umn.edu
noord014@umn.edu
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Programming Software Code
Simulation Data
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Abstract
This repository exists to share the data and scripts used in the paper "Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops" by Madeline Murphy, Kyle Noordhoek, Sallye Gathmann, Paul Dauenhauer, and Christopher Bartel. The bulk of the files are contained within the `programmable-loop-directionality` folder with additional detailed information presented in the `README.md` files of each subfolder. Here we also include zips containing each of the Random Forest models that were trained along with the full grid searches generated during the study.
Description
The `programmable-loop-directionality` zip includes any (individual) plots which appear in our manuscript related to data statistics, feature importance, and model performance. The folder also includes the data and scripts used to generate and analyze the machine learning models as well as the data and scripts used to set-up/run the microkinetic models and generate the microkinetic model results (pre-machine learning analysis) Each subfolder contains a detailed README.md file that provides additional information related to the data and scripts contained within.
Referenced by
Murphy, M. A., Noordhoek, K., Gathmann, S. R., Dauenhauer, P. J. & Bartel, C. J. Catalytic resonance theory: forecasting the flow of programmable catalytic loops. Digit. Discov. (2025) https://doi.org/10.1039/D4DD00216D
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This work was supported as part of the Center for Programmable Energy Catalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences at the University of Minnesota under award #DE-SC0023464.
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Murphy, Madeline; Noordhoek, Kyle; Gathmann, Sallye; Dauenhauer, Paul; Bartel, Chris. (2024). Data for Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/bh14-3q71.
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rf-reg-models.zip
Contains the best estimator and full grid search results for the random forest regression models.
(3.35 GB)
Readme_Noordhoek_2024.txt
Description of data
(143.52 KB)
labeled-rf-models.zip
Contains the best estimator and full grid search results for the OP random forest models trained with feature labels. These are necessary for the counterfactual (DICE) analysis.
(2.08 GB)
programmable-loop-directionality.zip
Repository containing scripts and data used to generate figures and analysis for our manuscript.
(357.96 MB)
rf-clf-models.zip
Contains the best estimator and full grid search results for the random forest classification models.
(1.42 GB)
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