Decoding quantum states through nuclear magnetic resonance

Description

At low temperatures, many materials transition into an electronic phase which cannot be classified as a simple metal or insulator. So-called quantum phases of matter, like superconductors and spin liquids, are hard to study due to their fragile nature, making nonintrusive and indirect measurements important. We will explore the connection between electronic phases and nuclei in these materials via simulations of nuclear magnetic resonance (NMR). By using external magnetic pulses, the nuclear spins can be controlled, and their time-evolution (time-dependent magnetization) studied.

Duration

Total project length: 175 hours.

Task ideas

Implement a classification model that determines the type of electronic interaction based on only the time-dependent curve. How sensitive is this classification to noise?
Utilize a neural network to predict the strength, range, and dissipation parameters of a given magnetization curve.
Develop an algorithm which optimizes an applied pulse sequence to best estimate a specific physical parameter from a given material.

Expected results

Comparison between ML algorithms to determine the ones best suited to the selected problem, including optimization of hyper parameters. When applicable, the gradients or important features of the model will be analyzed to determine what aspects of the experimental signal are most important.

Test

Please use this link to access the test for this project. Tests will be published in March.

Requirements

Python and some experience in Machine Learning. For using the NMR simulation code, familiarity with CUDA (GPU) and the Julia language will be helpful.

Mentors

Stephen Carr (Brown University)
Vesna Mitrovic (Brown University)
Chandrasekhar Ramanathan (Dartmouth University)
Brad Marston (Brown University)
Charles Snider (Brown University)

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.