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

19 March 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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 Au13+ and Au11+ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.

Keywords

cluster
chemical transferability
neural network
molecular dynamics
DFT

Supplementary materials

Title
Description
Actions
Title
manuscript
Description
Actions
Title
SI for ACS
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.