Approaching the life of spins using a computer

Computation techniques for magnetic resonance, ranging from the basics of 1D spectra to many-spin systems and machine learning methods.
Computation simulation of spin systems has a rich history in magnetic resonance, and is a powerful tool for predicting behavior, interpreting data, and discovering new experimental techniques. In this section, blogs will present functions for simulating basic magnetic resonance experiments in multiple coding languages, techniques for developing more complex algorithms, and introduce a framework for utilizing machine learning to approach problems in spin physics.

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Approaching the life of spins using a computer

Computation techniques for magnetic resonance, ranging from the basics of 1D spectra to many-spin systems and machine learning methods

Postulates of Quantum Mechanics

This series of posts lays out the foundational postulates of quantum mechanics using the two-level system of spin 1/2 particles as our model. In additionto a mathematical description of these postulates, MATLAB and Python code is included to show how to translate these ideas in a coding environment.

These posts will provide the mathematical foundation for more complex concepts in quantum mechanics and magnetic resonance, and the building blocks for code development of spin dynamics.