In this work, different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.
Allers, Joshua Paul, et al. "Machine learning prediction of self-diffusion in Lennard-Jones fluids." Journal of Chemical Physics, vol. 153, no. 3, Jul. 2020. https://doi.org/10.1063/5.0011512
Allers, Joshua Paul, Harvey, Jacob A., Garzon, Fernando Henry, & Alam, Todd M. (2020). Machine learning prediction of self-diffusion in Lennard-Jones fluids. Journal of Chemical Physics, 153(3). https://doi.org/10.1063/5.0011512
Allers, Joshua Paul, Harvey, Jacob A., Garzon, Fernando Henry, et al., "Machine learning prediction of self-diffusion in Lennard-Jones fluids," Journal of Chemical Physics 153, no. 3 (2020), https://doi.org/10.1063/5.0011512
@article{osti_1670740,
author = {Allers, Joshua Paul and Harvey, Jacob A. and Garzon, Fernando Henry and Alam, Todd M.},
title = {Machine learning prediction of self-diffusion in Lennard-Jones fluids},
annote = {In this work, different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.},
doi = {10.1063/5.0011512},
url = {https://www.osti.gov/biblio/1670740},
journal = {Journal of Chemical Physics},
issn = {ISSN 0021-9606},
number = {3},
volume = {153},
place = {United States},
publisher = {American Institute of Physics (AIP)},
year = {2020},
month = {07}}