Multi-Species Multi-Channel (MSMC): An Ab Initio- based Parallel Thermodynamic and Kinetic Code for Complex Chemical Systems

2015 ◽  
Vol 47 (9) ◽  
pp. 564-575 ◽  
Author(s):  
Minh V. Duong ◽  
Hieu T. Nguyen ◽  
Nghia Truong ◽  
Thong N.-M. Le ◽  
Lam K. Huynh
Keyword(s):  
Ab Initio ◽  
Complex Chemical ◽  
Chemical Systems

scholarly journals Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

2020 ◽  
Author(s):  
Jinzhe Zeng ◽  
Linfeng Zhang ◽  
Han Wang ◽  
Tong Zhu
Keyword(s):  
Ab Initio ◽  
Large Scale ◽  
Md Simulation ◽  
Learning Algorithm ◽  
Chemical Space ◽  
Md Simulations ◽  
Training Dataset ◽  
Complex Chemical ◽  
Pyrolysis Mechanism ◽  
Chemical Systems

<div> <div> <div> <p>Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of PES of both accurate and efficent has attracted significant effort in the past two decades. Recently developed Deep Potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training dataset. In this work, a dataset construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimize the redundancy of the dataset. This greatly reduces the cost of computational resources required by ab initio calculations. Based on this method, we constructed a dataset for the pyrolysis of n-dodecane, which contains 35,496 structures. The reactive MD simulation with the DP model trained based on this dataset revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this dataset shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training datasets for similar systems. </p> </div> </div> </div>

scholarly journals Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

2020 ◽  
Author(s):  
Jinzhe Zeng ◽  
Linfeng Zhang ◽  
Han Wang ◽  
Tong Zhu
Keyword(s):  
Ab Initio ◽  
Large Scale ◽  
Md Simulation ◽  
Learning Algorithm ◽  
Chemical Space ◽  
Md Simulations ◽  
Training Dataset ◽  
Complex Chemical ◽  
Pyrolysis Mechanism ◽  
Chemical Systems

<div> <div> <div> <p>Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of PES of both accurate and efficent has attracted significant effort in the past two decades. Recently developed Deep Potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training dataset. In this work, a dataset construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimize the redundancy of the dataset. This greatly reduces the cost of computational resources required by ab initio calculations. Based on this method, we constructed a dataset for the pyrolysis of n-dodecane, which contains 35,496 structures. The reactive MD simulation with the DP model trained based on this dataset revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this dataset shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training datasets for similar systems. </p> </div> </div> </div>

scholarly journals Topology-based Visualization of Transformation Pathways in Complex Chemical Systems

2011 ◽  
Vol 30 (3) ◽  
pp. 663-672 ◽  
Author(s):  
K. Beketayev ◽  
G.H. Weber ◽  
M. Haranczyk ◽  
P.-T. Bremer ◽  
M. Hlawitschka ◽  
...  
Keyword(s):  
Complex Chemical ◽  
Chemical Systems ◽  
Transformation Pathways

Analysis of the Bromate−Sulfite−Ferrocyanide pH Oscillator Using the Particle Filter: Toward the Automated Modeling of Complex Chemical Systems

2010 ◽  
Vol 114 (37) ◽  
pp. 10090-10096 ◽  
Author(s):  
Naoya Sato ◽  
Hiroshi H. Hasegawa ◽  
Rika Kimura ◽  
Yoshihito Mori ◽  
Noriaki Okazaki
Keyword(s):  
Particle Filter ◽  
Automated Modeling ◽  
Complex Chemical ◽  
Chemical Systems

scholarly journals From Supramolecular Chemistry to Complex Chemical Systems

2019 ◽  
Vol 25 (58) ◽  
pp. 13229-13230 ◽  
Author(s):  
Paolo Samorì ◽  
Nicolas Giuseppone
Keyword(s):  
Supramolecular Chemistry ◽  
Complex Chemical ◽  
Chemical Systems

scholarly journals Vacuum Ultraviolet Photoionization of Complex Chemical Systems

2016 ◽  
Vol 67 (1) ◽  
pp. 19-40 ◽  
Author(s):  
Oleg Kostko ◽  
Biswajit Bandyopadhyay ◽  
Musahid Ahmed
Keyword(s):  
Vacuum Ultraviolet ◽  
Complex Chemical ◽  
Chemical Systems
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