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Viscosity in water from first-principles and deep-neural-network simulations

Cesare Malosso1*, Linfeng Zhang2,3, Roberto Car4,2, Stefano Baroni1,5, Davide Tisi1

1 SISSA - Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy

2 Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA

3 DP Technology, Beijing 100080, People’s Republic of China

4 Department of Chemistry, Department of Physics, and Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, NJ 08544, USA

5 CNR Istituto Officina dei Materiali, SISSA unit, 34136 Trieste, Italy

* Corresponding authors emails:
DOI10.24435/materialscloud:3m-e6 [version v1]

Publication date: Jun 03, 2022

How to cite this record

Cesare Malosso, Linfeng Zhang, Roberto Car, Stefano Baroni, Davide Tisi, Viscosity in water from first-principles and deep-neural-network simulations, Materials Cloud Archive 2022.69 (2022), doi: 10.24435/materialscloud:3m-e6.


We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the strongly-constrained and appropriately-normed SCAN-DFT functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN DFT predictions of the shear viscosity of water are in very good agreement with experiments.

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564 Bytes Information on the files and instructions.
1.0 KiB Archive with data to generate the main plots in the paper.
76.5 MiB Archive with the machine-learning model and the dataset used to train it.
252.6 MiB ab initio time series of the stress series.


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viscosity molecular dynamics simulation ab initio machine learning neural networks water transport

Version history:

2022.76 (version v2) Jun 10, 2022 DOI10.24435/materialscloud:x7-b0
2022.69 (version v1) [This version] Jun 03, 2022 DOI10.24435/materialscloud:3m-e6