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Title: Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential"
Contributors: Car, Roberto
Panagiotopoulos, Athanassios
Muniz, Maria Carolina
Keywords: machine learning models
molecular simulations
many-body potential
Issue Date: 27-Sep-2023
Publisher: Princeton University
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.
Appears in Collections:Research Data Sets

Files in This Item:
File Description SizeFormat 
DPMDMBpolreadme.txt4.43 kBTextView/Download
DPMD-MB-pol-dataspace.zip469.26 MBUnknownView/Download

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