Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
© 2020 Author(s). Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models...
Main Authors: | Ruza, Jurgis, Wang, Wujie, Schwalbe-Koda, Daniel, Axelrod, Simon, Harris, William H, Gómez-Bombarelli, Rafael |
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Other Authors: | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
Format: | Article |
Language: | English |
Published: |
AIP Publishing
2022
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Online Access: | https://hdl.handle.net/1721.1/142527 |
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