Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"

Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Issue date: 2020
Rights:
Creative Commons Attribution 4.0 International (CC BY)
Cite as:
Gartner, Thomas III, Zhang, Linfeng, Piaggi, Pablo, Car, Roberto, Panagiotopoulos, Athanassios, & Debenedetti, Pablo. (2020). Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water" [Data set]. Princeton University. https://doi.org/10.34770/45m3-am91
@electronic{gartner_thomas_iii_2020,
  author      = {Gartner, Thomas III and
                Zhang, Linfeng and
                Piaggi, Pablo and
                Car, Roberto and
                Panagiotopoulos, Athanassios and
                Debenedetti, Pablo},
  title       = {{Data from "Signatures of a liquid-liquid
                 transition in an ab initio deep neural
                network model for water"}},
  publisher   = {{Princeton University}},
  year        = 2020,
  url         = {https://doi.org/10.34770/45m3-am91}
}
Description:

This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.

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