Characterizing Molecular-Dynamics Simulations Using Non-Local Spatial-Temporal Metrics. Liu, J., Yao, K., Nakano, A., & Rajak, P. In Proceedings of the 2017 International Conference on Scientific Computing, pages 155–158, 2017. abstract bibtex Improving the quality of molecular dynamics simulations requires optimization, and the choice of objectives heavily impacts parameter search. Current work with reactive molecular dynamics (RMD) uses simple atomic bond counts as optimization targets, but these metrics have weaknesses. Here we propose non-local spatial-temporal approaches for describing simulation states and for characterizing simulation quality as compared to the ground truth. Non-local metrics look beyond individual atoms and consider molecular structure when describing a simulation state. Spatial-temporal metrics measure how the simulation state evolves over time instead of focusing solely on the current timestep. Using these new metrics in optimization may enable generation of higher quality simulations.
@inproceedings{liu_characterizing_2017,
title = {Characterizing {Molecular}-{Dynamics} {Simulations} {Using} {Non}-{Local} {Spatial}-{Temporal} {Metrics}},
abstract = {Improving the quality of molecular dynamics simulations requires optimization, and the choice of objectives heavily impacts parameter search. Current work with reactive molecular dynamics (RMD) uses simple atomic bond counts as optimization targets, but these metrics have weaknesses. Here we propose non-local spatial-temporal approaches for describing simulation states and for characterizing simulation quality as compared to the ground truth. Non-local metrics look beyond individual atoms and consider molecular structure when describing a simulation state. Spatial-temporal metrics measure how the simulation state evolves over time instead of focusing solely on the current timestep. Using these new metrics in optimization may enable generation of higher quality simulations.},
booktitle = {Proceedings of the 2017 {International} {Conference} on {Scientific} {Computing}},
author = {Liu, Jeremy and Yao, Ke-Thia and Nakano, Aiichiro and Rajak, Pankaj},
year = {2017},
pages = {155--158},
}
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