Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations

2018 ◽  
Vol 122 (44) ◽  
pp. 10158-10171 ◽  
Author(s):  
Matti Hellström ◽  
Michele Ceriotti ◽  
Jörg Behler
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Sodium Hydroxide ◽  
Quantum Effects ◽  
Dynamics Simulations ◽  
Nuclear Quantum Effects

scholarly journals “On-the-fly” coupled cluster path-integral molecular dynamics: impact of nuclear quantum effects on the protonated water dimer

2015 ◽  
Vol 17 (22) ◽  
pp. 14355-14359 ◽  
Author(s):  
Thomas Spura ◽  
Hossam Elgabarty ◽  
Thomas D. Kühne
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Path Integral ◽  
Effective Potential ◽  
Water Dimer ◽  
Coupled Cluster ◽  
Single Well ◽  
Cluster Path ◽  
Dynamics Simulations ◽  
Nuclear Quantum Effects

“On-the-fly” coupled cluster-based path-integral molecular dynamics simulations predict that the effective potential of the protonated water–dimer has a single-well only.

Neural network potential for Zr-Rh system by machine learning

2021 ◽  
Author(s):  
Kun Xie ◽  
Chong Qiao ◽  
Hong Shen ◽  
Riyi Yang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mechanical Property ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Features ◽  
Ab Initio Molecular Dynamics ◽  
The Neural Network ◽  
Dynamics Simulations ◽  
Good Agreement

Abstract Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

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