Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp018w32r891v
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | Car, Roberto | - |
dc.contributor | Panagiotopoulos, Athanassios | - |
dc.contributor.author | Muniz, Maria Carolina | - |
dc.date.accessioned | 2023-09-27T15:53:17Z | - |
dc.date.available | 2023-09-27T15:53:17Z | - |
dc.date.created | 2023-09-10 | - |
dc.date.issued | 2023-09-27 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp018w32r891v | - |
dc.identifier.uri | https://doi.org/10.34770/vs79-fg55 | - |
dc.description.abstract | This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Princeton University | en_US |
dc.rights | CC-BY 4.0 | en_US |
dc.subject | machine learning models | en_US |
dc.subject | molecular simulations | en_US |
dc.subject | water | en_US |
dc.subject | many-body potential | en_US |
dc.title | Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential" | en_US |
dc.type | Dataset | en_US |
Appears in Collections: | Research Data Sets |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
DPMDMBpolreadme.txt | 4.43 kB | Text | View/Download | |
DPMD-MB-pol-dataspace.zip | 469.26 MB | Unknown | View/Download |
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