Publications by authors named "Toby G Perring"
Article Synopsis
- Deep neural networks are powerful tools for learning complex relationships in data, but they face challenges in scientific contexts like inelastic neutron scattering due to limited labeled data, uncertainty quantification, and interpretability.
- The study uses simulated data to train a deep neural network to differentiate between two magnetic exchange models in a half-doped manganite, showcasing the model's capabilities.
- By applying uncertainty quantification methods and class activation maps, the research highlights the importance of realistic training data and identifies key features in the data that influence the network's classification outcomes.
Spin liquids are highly correlated yet disordered states formed by the entanglement of magnetic dipoles. Theories define such states using gauge fields and deconfined quasiparticle excitations that emerge from a local constraint governing the ground state of a frustrated magnet. For example, the '2-in-2-out' ice rule for dipole moments on a tetrahedron can lead to a quantum spin ice in rare-earth pyrochlores.
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- Superconductivity in FeSe arises from a nematic phase that disrupts the four-fold rotational symmetry of the iron plane, potentially due to various magnetic and orbital influences.
- Inelastic neutron scattering reveals strong spin excitations at specific wave vectors in the normal state, showing a change in anisotropy at different energy levels.
- The study underscores the significant electronic anisotropy in FeSe's nematic phase and suggests that spin fluctuations contribute to a highly anisotropic superconducting gap.