CCS: A software framework to generate two-body potentials using Curvature Constrained Splines

Abstract 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 summary 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 are 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.