Rana Siddhuram, Sankar Manoj Uday, Lourderaj Upakarasamy, Sathyamurthy Narayanasami
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.
J Comput Chem. 2025 Sep 15;46(24):e70220. doi: 10.1002/jcc.70220.
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.
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。
