Abstract We present an open-source software package, NIC-CAGE (Novel Implementation of Constrained Calculations for Automated Generation of Excitations), for predicting quantum optimal control fields in photo-excited chemical systems. Our approach utilizes newly derived analytic gradients for maximizing the transition probability (based on a norm-conserving Crank–Nicolson propagation scheme) for driving a system from a known initial quantum state to another desired state. The NIC-CAGE code is written in the MATLAB and Python programming environments to aid in its readability and general accessibility to both users and practitioners. Throughout this work, we provide several examples and outputs on a variety of different potentials, propagation times, and user-defined parameters to demonstrate the robustness of the NIC-CAGE software package. As such, the use of this predictive tool by both experimentalists and theorists could lead to further advances in both understanding and controlling the dynamics of photo-excited systems. Program summary Program Title: NIC-CAGE CPC Library link to program files:https://dx.doi.org/10.17632/82jcpk5svt.1 Licensing provisions: GNU General Public License 3 Programming language: MATLAB or Python Supplementary material: Comparisons of propagated wavefunctions obtained from analytical π pulses vs wavefunctions resulting from numerically optimized electric fields predicted by the NIC-CAGE program Nature of problem: The NIC-CAGE software package utilizes analytic Crank–Nicolson gradients to compute optimized (and constrained) electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large ( ≈ 1 ) transition probability. Solution method: Analytic gradients, Crank–Nicolson propagation, and gradient ascent optimization

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NIC-CAGE：用于预测光激发化学系统中最佳控制场的开源软件包

摘要 我们提出了一个开源软件包 NIC-CAGE（用于自动生成激发的约束计算的新实现），用于预测光激发化学系统中的量子最优控制场。我们的方法利用新导出的解析梯度来最大化转移概率（基于范数守恒的 Crank-Nicolson 传播方案），以将系统从已知的初始量子状态驱动到另一个所需的状态。NIC-CAGE 代码是在 MATLAB 和 Python 编程环境中编写的，以帮助用户和从业人员提高其可读性和一般可访问性。在整个工作中，我们提供了关于各种不同电位、传播时间和用户定义参数的几个示例和输出，以证明 NIC-CAGE 软件包的鲁棒性。因此，实验家和理论家使用这种预测工具可以在理解和控制光激发系统的动力学方面取得进一步进展。程序摘要程序名称：NIC-CAGE CPC 库程序文件链接：https://dx.doi.org/10.17632/82jcpk5svt.1 许可条款：GNU 通用公共许可证 3 编程语言：MATLAB 或 Python 补充材料：传播的比较从分析 π 脉冲获得的波函数 vs 由 NIC-CAGE 程序预测的数值优化电场产生的波函数 问题性质：NIC-CAGE 软件包利用分析 Crank-Nicolson 梯度来计算优化（和约束）电场，该电场可以将系统从已知的初始振动本征态驱动到具有大 (≈1) 跃迁概率的指定最终量子态。求解方法：解析梯度、Crank-Nicolson 传播和梯度上升优化