Suppr超能文献

势能曲线和曲面的人工神经网络拟合:1/R难题

Artificial Neural Networks Fitting of Potential Energy Curves and Surfaces: The 1/R Conundrum.

作者信息

Rana Siddhuram, Sankar Manoj Uday, Lourderaj Upakarasamy, Sathyamurthy Narayanasami

机构信息

School of Chemical Sciences, National Institute of Science Education and Research (NISER) Bhubaneswar, An OCC of Homi Bhabha National Institute, Khurdha, India.

Indian Institute of Science Education and Research Mohali, Manauli, India.

出版信息

J Comput Chem. 2025 Sep 15;46(24):e70220. doi: 10.1002/jcc.70220.

Abstract

Within the Born-Oppenheimer approximation, the potential energy of a molecular system is written as a sum of electronic energy and nuclear-nuclear repulsion energy terms. The potential energy surface (PES), computed ab initio, as a function of bond distances and bond angles, has traditionally been represented using analytic functions and/or interpolation methods. We show here that the ab initio computed electronic energy values of a molecular system can be fitted more accurately than the corresponding potential energy values using the artificial neural network methodology. The exact Coulombic internuclear repulsion energy can be added subsequently to the fitted electronic energy to obtain an accurate PES.

摘要

在玻恩-奥本海默近似下,分子系统的势能被写成电子能量和核-核排斥能项的总和。从头计算得到的作为键长和键角函数的势能面(PES),传统上一直使用解析函数和/或插值方法来表示。我们在此表明,使用人工神经网络方法,分子系统的从头计算电子能量值能够比相应的势能值更精确地拟合。随后可以将精确的库仑核间排斥能加到拟合得到的电子能量上,以获得精确的PES。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b74/12434390/dfae8484eae4/JCC-46-0-g007.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验