General Multiobjective Force Field Optimization Framework, with Application to Reactive Force Fields for Silicon Carbide

2014 ◽  
Vol 10 (4) ◽  
pp. 1426-1439 ◽  
Author(s):  
Andres Jaramillo-Botero ◽  
Saber Naserifar ◽  
William A. Goddard
Keyword(s):  
Silicon Carbide ◽  
Force Field ◽  
Force Fields ◽  
Reactive Force ◽  
Optimization Framework ◽  
Reactive Force Fields

Reactive Force Fields for Aqueous and Interfacial Magnesium Carbonate Formation

2021 ◽  
Author(s):  
Siavash Zare ◽  
Mohammad Javad Abdolhosseini Qomi
Keyword(s):  
Force Field ◽  
Mineral Water ◽  
Force Fields ◽  
Magnesium Carbonate ◽  
Magnesium Ions ◽  
Reactive Force ◽  
Carbonate Formation ◽  
Reactive Force Fields ◽  
Interfacial Force

We develop Mg/C/O/H ReaxFF parameter sets for two environments: an aqueous force field for magnesium ions in solution and an interfacial force field for minerals and mineral-water interfaces. Since magnesium...

scholarly journals Neural network reactive force field for C, H, N, and O systems

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pilsun Yoo ◽  
Michael Sakano ◽  
Saaketh Desai ◽  
Md Mahbubul Islam ◽  
Peilin Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Density Functional ◽  
Force Fields ◽  
High Energy ◽  
Reactive Force ◽  
Dispersion Interactions ◽  
Reactive Force Field ◽  
Wide Range ◽  
Reactive Force Fields

AbstractReactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.

scholarly journals Ion-matter interactions by MD simulations making use of reactive force fields

2010 ◽  
Vol 43 (1-2) ◽  
pp. 112-115
Author(s):  
P. Philipp ◽  
Y. Yue ◽  
T. Wirtz ◽  
J. Kieffer
Keyword(s):  
Force Fields ◽  
Md Simulations ◽  
Reactive Force ◽  
Reactive Force Fields
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