Conveners
What electron configurations are possible in materials?
- Vesna Mitrovic
Description
In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here we study how machine learning can extract material parameters and help interpret magnetic response experiments. Although we consider only traditional spin-echo protocols at ideal pulsing, this work develops a framework for testing the efficacy of magnetic probes of electronic features. After applying standard machine-learning techniques to a large dataset of time-series simulations, if the initial parameters are predicted at a rate better than random guessing, then one can surmise that some amount of information is obtained by the proposed experiment. Moreover, a guide for interpreting real measurements is developed from feature ranking of the simulated data.. In this case, we compare the “automatically” generated approach to the previous analytical treatment and highlight improvements that the data-driven approach provides. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials.
Please register to receive access to full Workshop materials. Materials will be available using the email address you entered to register.
