Introduction

Welcome to the ML gas-surface tutorial! Here, you can find instructions on how to:

• use machine learning interatomic potentials (MLIPs) to run molecular dynamics (MD) simulations,
• perform adaptive sampling to build your database for gas-surface ML models.

References

If you found this tutorial helpful, please cite the following reference:

W. G. Stark, J. Westermayr, O. A. Douglas-Gallardo, J. Gardner, S. Habershon, R. J. Maurer, Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities, J. Phys. Chem. C, 127, 50, 24168–24182, (2023) [arXiv] [journal]

@misc{stark_machine_2023,
	title = {Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities},
	author = {Stark, Wojciech G. and Westermayr, Julia and Douglas-Gallardo, Oscar A. and Gardner, James and Habershon, Scott and Maurer, Reinhard J.},
	volume = {127},
	number = {50},
	pages = {24168–24182},
	year = {2023},
	publisher = {J. Phys. Chem. C},
	doi = {10.1021/acs.jpcc.3c06648}, 
	url = {https://doi.org/10.1021/acs.jpcc.3c06648}
}