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 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.

中文翻译：

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CCS：使用曲率约束样条生成二体势的软件框架

摘要 我们开发了一种使用曲率约束样条 (CCS) 拟合数据的自动化高效方案，以构建准确的二体势。该方法能够构建无振荡但灵活的潜力。我们表明优化问题是凸的，并且可以简化为标准的二次规划 (QP) 问题。这些改进通过从 ab initio 数据开发 Ne 的双体势来证明。我们还概述了该方法的可能扩展。程序摘要 程序名称：CCS CPC 库程序文件链接：http://dx.doi.org/10.17632/7dt5nzxgbs.1 开发者存储库链接：http://github.com/aksam432/CCS 许可条款：GPLv3 编程语言： Python 外部例程/库：NumPy、matplotlib、ASE、CVXOPT 问题性质：从头算量子化学方法通常在计算上非常昂贵。为了缓解这个问题，有必要开发有效的经验和半经验方法。二体势在经验和半经验方法中无处不在。求解方法：CCS 包提供了一种新的策略来获得准确的二体电位。势被描述为具有曲率约束的三次样条。