[Published online Journal of Computer Chemistry, Japan Vol.23, 65-67, by J-STAGE]
<Title:> ポリスチレン解重合のシミュレーション方法の検討
<Author(s):> 三枝 俊亮
<Corresponding author E-Mill:> mieda.sd(at)om.asahi-kasei.co.jp
<Abstract:> To verify the possibility to simulate depolymerization, simulations of the depolymerization of polystyrene were performed. Molecular Dynamics simulations (MD) using neural network potentials were found to be similar in accuracy to MD using density functional theory calculations. It was also found that long-time simulations using neural network potential-MD predicted styrene monomer yields close to those obtained experimentally, and that the monomer yields tended to decrease with increasing pressure.
<Keywords:> Key words depolymerization of polystyrene, long-time simulation using NNP-MD, ReaxFF, DFT-MD, Neural Network Potential
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/3/23_2024-0017/_article/-char/ja/
<Title:> ポリスチレン解重合のシミュレーション方法の検討
<Author(s):> 三枝 俊亮
<Corresponding author E-Mill:> mieda.sd(at)om.asahi-kasei.co.jp
<Abstract:> To verify the possibility to simulate depolymerization, simulations of the depolymerization of polystyrene were performed. Molecular Dynamics simulations (MD) using neural network potentials were found to be similar in accuracy to MD using density functional theory calculations. It was also found that long-time simulations using neural network potential-MD predicted styrene monomer yields close to those obtained experimentally, and that the monomer yields tended to decrease with increasing pressure.
<Keywords:> Key words depolymerization of polystyrene, long-time simulation using NNP-MD, ReaxFF, DFT-MD, Neural Network Potential
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/3/23_2024-0017/_article/-char/ja/
