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Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model
Abstract Machine learning (ML) is a promising tool for the detection of phases of matter.
However, ML models are also known for their black-box construction, which hinders …
However, ML models are also known for their black-box construction, which hinders …
[PDF][PDF] tightbinder: A Python package for semi-empirical tight-binding models of crystalline and disordered solids
Summary tightbinder is a Python package for Slater-Koster, semi-empirical tight-binding
calculations of the electronic structure of solids. Tight-binding models are ubiquitous in …
calculations of the electronic structure of solids. Tight-binding models are ubiquitous in …
[PDF][PDF] Design of plasmonic superconducting transition-edge-sensors with neural networks
We demonstrate the use of neural networks (NN) to improve the design of plasmonic
nanostructures. The scattering properties of a plasmonic nanostructure calculated by a slow …
nanostructures. The scattering properties of a plasmonic nanostructure calculated by a slow …