TY - JOUR
T1 - Viscosity in water from first-principles and deep-neural-network simulations
AU - Malosso, Cesare
AU - Zhang, Linfeng
AU - Car, Roberto
AU - Baroni, Stefano
AU - Tisi, Davide
N1 - Funding Information:
C.M., S.B., and D.T. are grateful to Federico Grasselli, Paolo Pegolo, and Riccardo Bertossa for enlightening discussions throughout the completion of this work. This work was partially funded by the EU through the MAX Centre of Excellence for supercomputing applications (Project No. 824143) and the Italian MUR, through the PRIN grant FERMAT. The work at Princeton University was supported by the Computational Chemical Sciences Center “Chemistry in Solution and at Interfaces” funded by the US Department of Energy under Award No. DE-SC0019394.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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) 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 predictions of the shear viscosity of water are in very good agreement with experiments.
AB - 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) 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 predictions of the shear viscosity of water are in very good agreement with experiments.
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U2 - 10.1038/s41524-022-00830-7
DO - 10.1038/s41524-022-00830-7
M3 - Article
AN - SCOPUS:85133270334
VL - 8
JO - npj Computational Materials
JF - npj Computational Materials
SN - 2057-3960
IS - 1
M1 - 139
ER -