Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential"

Muniz, Maria Carolina
Issue date: 27 September 2023
Cite as:
Muniz, Maria Carolina. (2023). Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential" [Data set]. Princeton University. https://doi.org/10.34770/vs79-fg55
@electronic{muniz_maria_carolina_2023,
  author      = {Muniz, Maria Carolina},
  title       = {{Data from "A Neural Network Water Model
                Based on the MB-pol Many-Body Potential"
                }},
  publisher   = {{Princeton University}},
  year        = 2023,
  url         = {https://doi.org/10.34770/vs79-fg55}
}
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.

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# Filename Description Filesize
1 DPMDMBpolreadme.txt 4.54 KB
2 DPMD-MB-pol-dataspace.zip 481 MB