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http://arks.princeton.edu/ark:/88435/dsp01b5644v47m
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DC Field | Value | Language |
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dc.contributor.author | Gartner, Thomas III | - |
dc.contributor.author | Zhang, Linfeng | - |
dc.contributor.author | Piaggi, Pablo | - |
dc.contributor.author | Car, Roberto | - |
dc.contributor.author | Panagiotopoulos, Athanassios | - |
dc.contributor.author | Debenedetti, Pablo | - |
dc.date.accessioned | 2020-07-20T20:32:01Z | - |
dc.date.available | 2020-07-20T20:32:01Z | - |
dc.date.issued | 2020-07 | - |
dc.identifier.uri | https://www.dropbox.com/sh/o883p0xjgcz92y6/AAD4zmPAP-5h77npA4kKcBFva?dl=0 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp01b5644v47m | - |
dc.identifier.uri | https://doi.org/10.34770/45m3-am91 | - |
dc.description | The DNN model for water was generated using the DP-GEN methodology (https://github.com/deepmodeling/dpgen) and DeepMD-kit v1.0 (https://github.com/deepmodeling/deepmd-kit) software. Simulations were performed using the LAMMPS molecular simulation software v7Aug19 (https://lammps.sandia.gov/) patched with the Plumed 2.0 advanced sampling software (https://www.plumed.org/). Detailed methods are as described in Gartner et al, (in preparation 2020). Detailed installation instructions for all software are available at the above linked webpages. Please download the data sets from Dropbox using https://www.dropbox.com/sh/o883p0xjgcz92y6/AAD4zmPAP-5h77npA4kKcBFva?dl=0 | en_US |
dc.description.abstract | This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab-inito 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. | en_US |
dc.description.sponsorship | This work was supported by the “Chemistry in Solution and at Interfaces” (CSI) Center funded by the U.S. Department of Energy Award DE-SC001934. P.M.P. was supported by an Early Postdoc.Mobility fellowship from the Swiss National Science Foundation. Calculations were performed using Terascale Infrastructure for Groundbreaking Research in Engineering and Science (TIGRESS) resources at Princeton University. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | molecular simulation | en_US |
dc.subject | machine learning | en_US |
dc.subject | water | en_US |
dc.subject | liquid-liquid transition | en_US |
dc.subject | statistical mechanics | en_US |
dc.subject | advanced sampling | en_US |
dc.subject | Deep-potential molecular dynamics | en_US |
dc.title | Data from "Signatures of a liquid-liquid transition in an ab-inito neural network model for water" | en_US |
dc.type | Dataset | en_US |
Appears in Collections: | Research Data Sets |
Files in This Item:
File | Description | Size | Format | |
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Water-LLT-DPMDreadme.txt | 4.95 kB | Text | View/Download |
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