Chain fragmentation and fragment diffusion at the glass transition

Hellmann, G. P.; Hellmann, E. H.; Rennie, Adrian R.

doi:10.1007/bfb0116480

Cited by 1 publication

(2 citation statements)

References 2 publications

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“…This suggests that our neural network potentials should be able to capture the non‐additive contribution to the three‐body interactions for helium trimers. And we also find that the nonadditive contribution to three‐body interactions of helium increases with a decrease of temperature, which corresponds to the results of previous studies 35 …”

Section: Resultssupporting

confidence: 91%

“…Helium 3 is located on the xy plane at a distance of

R_{13}

from the origin and an angle of

θ_{123}

about the origin. Due to the symmetry, we consider

0.75 Å \leq

R_{12}

\leq

R_{13}

\leq 20

Å and

θ

\leq

θ_{123}

\leq 180 °

, where

θ

* = arcsin(0.5

R_{12}

R_{13}

) 35 . Then, the three‐body intermolecular potential (

V_{three}

) is obtained by using the single point energies from quantum mechanical calculations and employing the following relation:

V_{three} ((),,, R_{12}, R_{13}, θ_{123}) goodbreak= E_{123} ((),,, R_{12}, R_{13}, θ_{123}) goodbreak- E_{123} ((),,, R_{cut}, R_{cut}, θ_{123}),

where

E_{123}

(

R_{12}

R_{13}

θ_{123}

) corresponds to the energy of helium trimer at (

R_{12}

R_{13}

θ_{123}

) and

R_{cut}

is 500 Å.…”

Section: Models and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The accurate estimation of the third virial coefficients for helium using three‐body neural network potentials

Kwon

Song

Woo

et al. 2022

Bulletin Korean Chem Soc

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The description of many‐body interactions is one of challenging problems in molecular dynamics simulations. Recently, neural network potentials have been spotlighted as an approach to describe many‐body interactions. In this study, we obtain the neural network potentials for three‐body interactions of helium using a deep learning method. We perform quantum calculations to obtain single point energies for helium trimers and obtain the neural network potentials for three‐body interactions by performing a deep learning method. In order to test the validity of the neural network three‐body interactions, we perform Mayer‐sampling Monte Carlo simulations and calculate third virial coefficients for helium. We show that the third virial coefficients obtained from three‐body neural network potentials are more accurate than those obtained from two‐body neural network potentials. The deep learning method in our study would be extended to obtain the high‐order virial coefficients for complex molecules.

show abstract

Section: Resultssupporting

confidence: 91%

“…Helium 3 is located on the xy plane at a distance of

R_{13}

from the origin and an angle of

θ_{123}

about the origin. Due to the symmetry, we consider

0.75 Å \leq

R_{12}

\leq

R_{13}

\leq 20

Å and

θ

\leq

θ_{123}

\leq 180 °

, where

θ

* = arcsin(0.5

R_{12}

R_{13}

) 35 . Then, the three‐body intermolecular potential (

V_{three}

) is obtained by using the single point energies from quantum mechanical calculations and employing the following relation:

V_{three} ((),,, R_{12}, R_{13}, θ_{123}) goodbreak= E_{123} ((),,, R_{12}, R_{13}, θ_{123}) goodbreak- E_{123} ((),,, R_{cut}, R_{cut}, θ_{123}),

where

E_{123}

(

R_{12}

R_{13}

θ_{123}

) corresponds to the energy of helium trimer at (

R_{12}

R_{13}

θ_{123}

) and

R_{cut}

is 500 Å.…”

Section: Models and Methodsmentioning

confidence: 99%

The accurate estimation of the third virial coefficients for helium using three‐body neural network potentials

Kwon

Song

Woo

et al. 2022

Bulletin Korean Chem Soc

View full text Add to dashboard Cite

show abstract

Chain fragmentation and fragment diffusion at the glass transition

Cited by 1 publication

References 2 publications

The accurate estimation of the third virial coefficients for helium using three‐body neural network potentials

The accurate estimation of the third virial coefficients for helium using three‐body neural network potentials

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