Kyle F Sunden
University of Wisconsin-{}-Madison

Blaise J Thompson
University of Wisconsin-{}-Madison

John C Wright
University of Wisconsin-{}-Madison

Abstract

Nonlinear multidimensional spectroscopy (MDS) is a powerful experimental technique used to interrogate complex chemical systems. MDS promises to reveal energetics, dynamics, and coupling features of and between the many quantum-mechanical states that these systems contain. In practice, simulation is typically required to connect measured MDS spectra with these microscopic physical phenomena. We present an open-source Python package, WrightSim, designed to simulate MDS. Numerical integration is used to evolve the system as it interacts with several electric fields in the course of a multidimensional experiment. This numerical approach allows WrightSim to fully account for finite pulse effects that are commonly ignored. WrightSim is made up of modules that can be exchanged to accommodate many different experimental setups. Simulations are defined through a Python interface that is designed to be intuitive for experimentalists and theorists alike. We report several algorithmic improvements that make WrightSim faster than previous implementations. We demonstrated the effect of parallelizing the simulation, both with CPU multiprocessing and GPU (CUDA) multithreading. Taken together, algorithmic improvements and parallelization have made WrightSim multiple orders of magnitude faster than previous implementations. WrightSim represents a large step towards the goal of a fast, accurate, and easy to use general purpose simulation package for multidimensional spectroscopy. To our knowledge, WrightSim is the first openly licensed software package for these kinds of simulations. Potential further improvements are discussed.

Keywords

Simulation, spectroscopy, PyCUDA, numerical integration, Quantum Mechanics, multidimensional

DOI

10.25080/Majora-4af1f417-00c

Bibtex entry

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Copyright © 2018 Kyle F Sunden et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.