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Found 6 papers, 2 papers with code
Weak-signal extraction enabled by deep-neural-network denoising of diffraction data
Here, we show how data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy.
Symmetry Indicators for Inversion-Symmetric Non-Hermitian Topological Band Structures
We characterize non-Hermitian band structures by symmetry indicator topological invariants.
Mesoscale and Nanoscale Physics
Introduction to Machine Learning for the Sciences
This is an introductory machine-learning course specifically developed with STEM students in mind.
Discovery of unconventional chiral charge order in kagome superconductor KV3Sb5
Here we use high-resolution scanning tunnelling microscopy (STM) to discover an unconventional charge order in a kagome material KV3Sb5, with both a topological band structure and a superconducting ground state.
Superconductivity Materials Science Strongly Correlated Electrons
Abelian topological order of $ν=2/5$ and $3/7$ fractional quantum Hall states in lattice models
Determining the statistics of elementary excitations supported by fractional quantum Hall states is crucial to understanding their properties and potential applications.
Strongly Correlated Electrons
Learning-based Defect Recognition for Quasi-Periodic Microscope Images
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device.
