Parametrization of nanoparticles: development of full-particle nanodescriptors

Nanoscale ◽  
2016 ◽  
Vol 8 (36) ◽  
pp. 16243-16250 ◽  
Author(s):  
K. Tämm ◽  
L. Sikk ◽  
J. Burk ◽  
R. Rallo ◽  
S. Pokhrel ◽  
...  
Keyword(s):  
Biological Activity ◽  
Metal Oxide ◽  
Force Field ◽  
Field Calculation ◽  
Force Field Calculation

A set of novel, theoretical full-particle descriptors for modeling, grouping or read-across of metal oxide NP properties and biological activity was developed based on the force-field calculation of the potential energies of whole NPs.

Force-field calculation and geometry of the HOOO radical

2013 ◽  
Vol 139 (9) ◽  
pp. 094301 ◽  
Author(s):  
Kohsuke Suma ◽  
Yoshihiro Sumiyoshi ◽  
Yasuki Endo
Keyword(s):  
Force Field ◽  
Field Calculation ◽  
Force Field Calculation

Force field calculation for inplane vibrations of ethylene usingcndo/Force method

Pramana ◽  
1982 ◽  
Vol 19 (4) ◽  
pp. 413-420 ◽  
Author(s):  
A Jothi ◽  
G Shanmugam ◽  
A Annamalai ◽  
Surjit Singh
Keyword(s):  
Force Field ◽  
Field Calculation ◽  
Force Method ◽  
Force Field Calculation

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

2019 ◽  
Author(s):  
Yasuharu Okamoto
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Data Driven ◽  
High Dimensional ◽  
Field Calculation ◽  
Force Field Calculations ◽  
Quantitative Accuracy ◽  
Force Field Calculation ◽  
Data Driven Approach ◽  
Dynamics Simulations

<p>High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au<sub>13</sub><sup>+</sup> and Au<sub>11</sub><sup>+</sup> clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.</p>

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