NIC-CAGE: An open-source software package for predicting optimal control fields in photo-excited chemical systems

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: http://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 pi 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 (approximate to 1) transition probability. Solution method: Analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

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 (approximate to 1) transition probability.