Reactive molecular dynamics for the [Cl–CH 3 –Br] − reaction in the gas phase and in solution: a comparative study using empirical and neural network force fields

2019 ◽  
Vol 1 (2) ◽  
pp. 024002 ◽  
Author(s):  
Sebastian Brickel ◽  
Akshaya K Das ◽  
Oliver T Unke ◽  
Haydar T Turan ◽  
Markus Meuwly
RSC Advances ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 4293-4299 ◽  
Author(s):  
Lei Chen ◽  
Ivan Sukuba ◽  
Michael Probst ◽  
Alexander Kaiser

Reactive self-sputtering from a Be surface is simulated using neural network trained forces with high accuracy. The key in machine learning from DFT calculations is a well-balanced and complete training set of energies and forces obtained by iterative refinement.


2011 ◽  
Vol 115 (14) ◽  
pp. 3964-3971 ◽  
Author(s):  
Michael B. Plazzer ◽  
David J. Henry ◽  
George Yiapanis ◽  
Irene Yarovsky

2013 ◽  
Vol 9 (S297) ◽  
pp. 353-355
Author(s):  
N. Patra ◽  
H. R. Sadeghpour

AbstractWe investigate the nucleation of carbon and hydrogen atoms in the gas phase to form large carbon chains, clusters and cages by reactive molecular dynamics simulations. We study how temperature, particle density, presence of hydrogen, and carbon inflow affect the nucleation of molecular moieties with different characteristics.


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