Results 141 to 150 of about 66,190 (276)

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

open access: yesAdvanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Intramolecular Charge‐Transfer Dopants Enable Isolated Triplet Excitons as Spin Qutrits in a Single Crystal

open access: yesAngewandte Chemie, EarlyView.
Photoexcitation of an intramolecular charge‐transfer molecule doped into single crystals of a structurally similar host molecule results in well‐ordered, spectrally addressable, and orientation‐tunable triplet excitons that maintain coherence at elevated temperatures.
Yaoyao Han   +6 more
wiley   +2 more sources

Influences of the Cu Substitution at Hg Site in Hg1 - xCuxBa2Ca2Cu3O8+δ Superconductors

open access: yesIraqi Journal of Physics, 2008
The influences of the Cu substitution at Hg site in the HgOd layer, upon the microstructure, Tc and oxygen content of Hg-1223 have been investigated. High temperature superconductor with a nominal composition Hg1-xCuxBa2Ca2Cu3O8 + δ for Cu ( 0 £ x £ 0.5)
M. M. Abbas
doaj  

Topological Materials and Related Applications

open access: yesAdvanced Electronic Materials, EarlyView.
This review covers topological materials—including topological insulators, quantum valley Hall and quantum spin Hall insulators, and topological Weyl and Dirac semimetals—as well as their most recent advancements in fields such as spintronics, electronics, photonics, thermoelectrics, and catalysis.
Carlo Grazianetti   +9 more
wiley   +1 more source

Fluctuaciones térmicas en la conductividad de superconductores de alta temperatura en presencia de campos magnéticos

open access: yesMomento, 2001
One very interesting characteristic of the type II superconductor materials is observed in the neighborhood of the transition between normal and superconductor states, which is related with the occurrence of new transport channels of superparticles still
Jairo Roa Rojas
doaj  

Advancing Energy Materials by In Situ Atomic Scale Methods

open access: yesAdvanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss   +21 more
wiley   +1 more source

How Particle Size Affects Consolidation Behavior, Strain and Properties of Li6PS5Cl Fast Ionic Conductors

open access: yesAdvanced Energy Materials, EarlyView.
The densification process of Li6PS5Cl powders with varying particles size distributions reveals differences in smaller and larger distributions. Higher strain is revealed for the smaller particle size distribution from X‐ray diffraction. Discrete element method simulations uncover that the reason for the higher strain is not the particle size itself ...
Vasiliki Faka   +14 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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