Results 191 to 200 of about 166,486,521 (293)

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

open access: yesAdvanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li   +6 more
wiley   +1 more source

Stable real-space invariants and topology beyond symmetry indicators. [PDF]

open access: yesNat Commun
Hwang Y   +6 more
europepmc   +1 more source

Type‐II Dirac Fermions in Monolayer In2O: Interplay of Magnetotransport, Spin Hall Effect, and Superconductivity

open access: yesAdvanced Science, EarlyView.
First‐principles calculations reveal that monolayer In2O${\rm In}_2{\rm O}$ hosts type‐II Dirac fermions near the Fermi level, which split into Weyl points under spin‐orbit coupling. The material exhibits negative and giant magnetoresistance, a pronounced spin Hall effect, and phonon‐mediated superconductivity at 1.5 K, establishing it as a unique ...
Qing‐Bo Liu   +6 more
wiley   +1 more source

Ferroelectric control of the Mott insulator-topological metal transition. [PDF]

open access: yesSci Adv
Niu M   +9 more
europepmc   +1 more source

Symmetry-enriched topological states of matter in insulators and semimetals

open access: yes, 2017
Topological states of matter are a novel family of phases that elude the conventional Landau paradigm of phase transitions. Topological phases are characterized by global topological invariants which are typically reflected in the quantization of physical observables.
openaire   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

Disentangling high harmonic generation from surface and bulk states of a topological insulator. [PDF]

open access: yesNat Commun
Li S   +11 more
europepmc   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

open access: yesAdvanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li   +8 more
wiley   +1 more source

Engineering of Crystal and Domain Structures in Epitaxial Y:HfO2 Thin Films by YSZ Substrate Miscut

open access: yesAdvanced Science, EarlyView.
We investigate how YSZ substrate miscut influences crystal structure and domain formation in epitaxial Y‐doped HfO2 thin films. Using magnetron sputtering, high‐resolution X‐ray diffraction, atomic‐resolution scanning transmission electron microscopy, and first‐principles calculations, we systematically examine the characteristics of thickness‐ and ...
Jun Young Lee   +12 more
wiley   +1 more source

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