This repository contains a collection of machine learning models for catalysis applications.
This model is described in detail in:
N. Artrith*, Z. Lin, and J. G Chen,
"Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning",
ACS Catal. 2020, 10, 9438−9444, DOI: https://doi.org/10.1021/acscatal.0c02089
S.R. Denny, Z. Lin, W.N. Porter, N. Artrith*, and J.G. Chen*,
"Machine Learning Prediction and Experimental Verification of Pt-Modified Nitride Catalysts for Ethanol Reforming with Reduced Precious Metal Loading" Applied Catalysis B: Environmental, 2022, 312, 121380: DOI https://doi.org/10.1016/j.apcatb.2022.121380
Please cite this reference if you make use of any parts of the source code or model or the DFT database.
*Contact: nartrith@atomistic.net
Subdirectory: ethanol-reforming
The scripts 01-activation-energy-model.py and
02-activity-and-selectivity-model.py have to be run sequentially. The
first script predicts transition-state energies based on DFT
thermochemical data. The second script predicts reforming activities
and selectivities based on the transition-state energies from script 1.
usage: 01-activation-energy-model.py [-h] [dft_data]
Construct ML Model 1 for predicting transition-state energies from
thermochemical DFT data and chemical information.
The model uses a combination of Random Forest Regression and Gaussian
Process Regression.
2019-11-10 Nongnuch Artrith
positional arguments:
dft_data CSV file with DFT data.
optional arguments:
-h, --help show this help message and exit
usage: 02-activity-and-selectivity-model.py [-h]
[dft_data] [transition_state_data]
[experimental_data]
Construct ML Model 2 for predicting catalytic activities and
selectivities.
The models are based on linear regression.
2019-11-10 Nongnuch Artrith
positional arguments:
dft_data CSV file with DFT data.
transition_state_data
CSV file with transition-state data from Model 1.
experimental_data CSV file with data from experimental characterization.
optional arguments:
-h, --help show this help message and exit
$ ./01-activation-energy-model.py
CV RMSE (RFR+GPR) = 0.31367854134356526
CV MAE (RFR+GPR) = 0.19685553022494306
$ ./02-activity-and-selectivity-model.py
Reforming Activity Model:
CV RMSE = 0.00360602875964415
CV MAE = 0.0033449441185262325
Output from script 1
- validation-TS-model-RFR+GPR.png
- validation-TS-model-RFR+GPR.pdf
- predicted-TS-RF+GPR.csv
Output from script 2
- validation-reforming-activity-model.png
- validation-reforming-activity-model.pdf
- predicted-reforming-activity.csv
- validation-reforming-selectivity-from-total-activity.png
- validation-reforming-selectivity-from-total-activity.pdf
- predicted-reforming-selectivity-from-total-activity.csv
- validation-reforming-selectivity-logit.png
- validation-reforming-selectivity-logit.pdf
- predicted-reforming-selectivity-logit.csv
DFT calculations and machine-learning model construction made use of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575 (allocation no. DMR14005). Calculations were also performed on the computational resources of the Center for Functional Nano- materials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE- SC0012704. We also acknowledge computing resources from Columbia University’s Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010. This article was sponsored by the Catalysis Center for Energy Innovation (CCEI), an Energy Frontier Research Center (EFRC) funded by the U.S. Department of Energy, Office of Basic Energy Sciences under Award Number DE-SC0001004. N.A. thanks Dr. Jose Garrido Torres and Dr. Mark S Hybertsen for discussions.