Authors: Akshay Krishna A. K., Eddie Wadbro, Christof Köhler, Pavlin Mitev, Peter Broqvist, and Jolla Kullgren
We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method.
Program Title: CCS
CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1
Developer’s repository link: http://github.com/aksam432/CCS
Licensing provisions: GPLv3
Programming language: Python
External routines/libraries: NumPy, matplotlib, ASE, CVXOPT
Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods.
Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.
Computer Physics Communications, 258, 107602, (2021);