Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Confinement Drives Valley Splitting above 4 K in Buried Silicon Quantum Wells. [PDF]
Degli Esposti D +5 more
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Protected Metallic Edge States in a Graphene-Capped Quantum Spin Hall System. [PDF]
Ngo HT +6 more
europepmc +1 more source
Ultranarrow Germanene Nanoribbons with Pentagonal Rings. [PDF]
Sun K, Silveira OJ, Foster AS, Kawai S.
europepmc +1 more source
Scanning Tunneling Spectroscopy on InAs-GaSb Esaki Diode Nanowire Devices during Operation. [PDF]
Persson O +5 more
europepmc +1 more source
Yu-Shiba-Rusinov-assisted Andreev transport in a molecular junction between superconductors. [PDF]
Mier C +7 more
europepmc +1 more source
Probing Type-II Multiferroicity in Monolayer NiBr<sub>2</sub>. [PDF]
Cahlík A +7 more
europepmc +1 more source
How the vortex lattice of a superconductor becomes disordered: a study by scanning tunneling spectroscopy. [PDF]
Zehetmayer M.
europepmc +1 more source
Tunable Luminescence from a Single Free-Base Porphyrin Molecule by Controlled Access to Optically Active States. [PDF]
Ammerman E, Krane N, Schuler B.
europepmc +1 more source

