Data for Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops

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Collection period

2023-07-01
2024-03-01

Date completed

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

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Dataset
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

https://chemrxiv.org/engage/chemrxiv/article-details/66841dae5101a2ffa82aebbb
Murphy, Madeline; Noordhoek, Kyle; Gathmann, Sallye; Dauenhauer, Paul; Bartel, Chris. (2024). Catalytic Resonance Theory: Forecasting the Flow of Programmable Catalytic Loops. UNDER REVIEW

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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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Suggested citation

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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