Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Small Molecular Systems

12 May 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We provide discussions and considerations on data quality, data sampling and data fitting for developing full-dimensional accurate potential energy surfaces with ample interpretative examples, centering on accuracy, efficiency, and generality.

Keywords

potential energy surface
PIP-NN
data sampling
data quality
data fitting

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.
Comment number 1, Jun Li: Oct 06, 2023, 02:23

This is a preprint of the following chapter: Jun Li and Yang Liu, Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-Dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions, published in Machine Learning in Molecular Sciences, edited by Chen Qu and Hanchao Liu, 2023, Springer Cham reproduced with permission of Springer Nature Switzerland AG 2023. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-37196-7_6.