Abstract
Liquid state nuclear magnetic resonance is the only class of magnetic resonance experiments for which the simulation problem is solved comprehensively for spin systems of any size. This paper contains a practical walkthrough for one of the many available simulation packages — Spinach. Its unique feature is polynomial complexity scaling: the ability to simulate large spin systems quantum mechanically and with accurate account of relaxation, diffusion, chemical processes, and hydrodynamics. This paper is a gentle introduction written with a PhD student in mind.
Highlights
Textbooks and introductory lectures make NMR simulation look deceptively simple: type in some Pauli matrices, make a Hamiltonian, compute the exponential, and that’s ostensibly it – their authors have done a wonderful job of making the subject easy to understand [1,2,3]
This paper is a practical walkthrough – it goes through the process of setting up and running liquid state NMR simu‐ lations in the order that most people would be doing it in practice
Many packages can generate a rea‐ sonable likeness of a 1D NMR spectrum for large spin systems, but complicated combinations of multi‐dimensional pulse sequences, advanced relaxation and kinetics treatments, shaped pulses and gradients, diffusion and flow are only available in Spinach
Summary
Textbooks and introductory lectures make NMR simulation look deceptively simple: type in some Pauli matrices, make a Hamiltonian, compute the exponential, and that’s ostensibly it – their authors have done a wonderful job of making the subject easy to understand [1,2,3]. Many packages can generate a rea‐ sonable likeness of a 1D NMR spectrum for large spin systems, but complicated combinations of multi‐dimensional pulse sequences, advanced relaxation and kinetics treatments, shaped pulses and gradients, diffusion and flow are only available in Spinach. This is the result of very recent theoretical developments, the primary ones being quantum mechanical simulation algorithms [7,8] that have much lower computational resource requirements than anything previously available, and the Fokker‐Planck equation for the spatial degrees of freedom [9,10]. The current public version requires Matlab R2016b or later with Parallel Computing Toolbox and Optimisation Toolbox installed
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