期刊
CHEMICAL PHYSICS LETTERS
卷 629, 期 -, 页码 40-45出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cplett.2015.04.019
关键词
-
资金
- National Foundation for Science and Technology Developments (NAFOSTED) [103.01-2013.28]
We present in this study a new and robust algorithm for feed-forward neural network (NN) fitting. This method is developed for the application in potential energy surface (PES) construction, in which simultaneous energy-gradient fitting is implemented using the well-established Levenberg-Marquardt (LM) algorithm. Three fitting examples are demonstrated, which include the vibrational PES of H2O, reactive PESs of O-3 and ClOOCl. In the three testing cases, our new LM implementation has been shown to work very efficiently. Not only increasing fitting accuracy, it also offers two other advantages: less training iterations are utilized and less data points are required for fitting. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据