Machine Learning for Performance Enhancement of Molecular Dynamics Simulations. Kadupitiya, J., Fox, G., C., & Jadhao, V. In volume 11537 LNCS, pages 116-130, 2019. Springer Verlag.
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Paper doi abstract bibtex
Paper doi abstract bibtex We explore the idea of integrating machine learning with simulations to enhance the performance of the simulation and improve its usability for research and education. The idea is illustrated using hybrid OpenMP/MPI parallelized molecular dynamics simulations designed to extract the distribution of ions in nanoconfinement. We find that an artificial neural network based regression model successfully learns the desired features associated with the output ionic density profiles and rapidly generates predictions that are in excellent agreement with the results from explicit molecular dynamics simulations. The results demonstrate that the performance gains of parallel computing can be further enhanced by using machine learning.
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abstract = {We explore the idea of integrating machine learning with simulations to enhance the performance of the simulation and improve its usability for research and education. The idea is illustrated using hybrid OpenMP/MPI parallelized molecular dynamics simulations designed to extract the distribution of ions in nanoconfinement. We find that an artificial neural network based regression model successfully learns the desired features associated with the output ionic density profiles and rapidly generates predictions that are in excellent agreement with the results from explicit molecular dynamics simulations. The results demonstrate that the performance gains of parallel computing can be further enhanced by using machine learning.},
bibtype = {inproceedings},
author = {Kadupitiya, Jcs and Fox, Geoffrey C. and Jadhao, Vikram},
doi = {10.1007/978-3-030-22741-8_9}
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