Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent
Abstract Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-023-01100-w |
| _version_ | 1827633691580432384 |
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| author | Ioan-Bogdan Magdău Daniel J. Arismendi-Arrieta Holly E. Smith Clare P. Grey Kersti Hermansson Gábor Csányi |
| author_facet | Ioan-Bogdan Magdău Daniel J. Arismendi-Arrieta Holly E. Smith Clare P. Grey Kersti Hermansson Gábor Csányi |
| author_sort | Ioan-Bogdan Magdău |
| collection | DOAJ |
| description | Abstract Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems. |
| first_indexed | 2024-03-09T15:04:05Z |
| format | Article |
| id | doaj.art-83f0340a9744414abf598a0e07892983 |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-03-09T15:04:05Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-83f0340a9744414abf598a0e078929832023-11-26T13:47:02ZengNature Portfolionpj Computational Materials2057-39602023-08-019111510.1038/s41524-023-01100-wMachine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solventIoan-Bogdan Magdău0Daniel J. Arismendi-Arrieta1Holly E. Smith2Clare P. Grey3Kersti Hermansson4Gábor Csányi5Engineering Laboratory, University of CambridgeDepartment of Chemistry–Ångström Laboratory, Uppsala UniversityYusuf Hamid Department of Chemistry, University of CambridgeYusuf Hamid Department of Chemistry, University of CambridgeDepartment of Chemistry–Ångström Laboratory, Uppsala UniversityEngineering Laboratory, University of CambridgeAbstract Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems.https://doi.org/10.1038/s41524-023-01100-w |
| spellingShingle | Ioan-Bogdan Magdău Daniel J. Arismendi-Arrieta Holly E. Smith Clare P. Grey Kersti Hermansson Gábor Csányi Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent npj Computational Materials |
| title | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent |
| title_full | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent |
| title_fullStr | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent |
| title_full_unstemmed | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent |
| title_short | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent |
| title_sort | machine learning force fields for molecular liquids ethylene carbonate ethyl methyl carbonate binary solvent |
| url | https://doi.org/10.1038/s41524-023-01100-w |
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