Results 121 to 130 of about 393,404 (281)

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

open access: yesAdvanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena   +3 more
wiley   +1 more source

Measuring the Hall Effect in Hysteretic Materials

open access: yesAdvanced Materials, EarlyView.
The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya   +6 more
wiley   +1 more source

Why Is the Mechanism Underlying the Chiral‐Induced Selectivity Effect Still Challenging?

open access: yesAdvanced Materials, EarlyView.
The chiral‐induced spin selectivity (CISS) effect is observed in many experimental configurations and for different materials. However, there are theoretical challenges in attempting to explain those results. A qualitative framework for explaining all the results is presented.
Ron Naaman, Yossi Paltiel
wiley   +1 more source

Resonant Domain Wall Dynamics in a Three‐Dimensional Magnetic Nano Double Helix

open access: yesAdvanced Materials, EarlyView.
3D magnetic nanostructures promise exciting possibilities for magnetization dynamics. However, experimental realizations remain scarce. In nanoprinted cobalt double helices, time‐resolved X‐ray microscopy reveals harmonic domain wall dynamics. Simulations identify the mode and additional higher‐frequency resonances, revealing a rich dynamic landscape ...
Pamela Morales‐Fernández   +15 more
wiley   +1 more source

Emergent Spin Supersolids in Frustrated Quantum Materials

open access: yesAdvanced Materials, EarlyView.
This review highlights developments in the study of spin super‐solids in frustrated quantum materials. Advanced experimental characterizations and computational studies enable a comprehensive understanding of the driving mechanisms of spin super‐solidity in various layered transition‐metal compounds, bridging materials, experiments, and theory aspects.
Yixuan Huang   +2 more
wiley   +1 more source

Topological fingerprints of commensurate order in twisted moiré lattices

open access: yesMaterials Today Quantum
Moiré patterns in twisted bilayer materials exhibit long-range periodic order that is highly sensitive to twist angle. Identifying commensurate angles from real-space atomic structures remains challenging, as spectral and geometric methods emphasize ...
Collin J. Kovacs   +5 more
doaj   +1 more source

QSPR Study of Critical Micelle Concentration of Anionic Surfactants Using Computational Molecular Descriptors†

open access: yes, 2016
A data set of 181 diverse anionic surfactants has been investigated to relate the logarithm of critical micelle concentration (cmc) to the molecular structure using Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA Pro) software.
Liliana Pacureanu (2490361)   +3 more
core   +1 more source

Weak In‐Plane Ferromagnetism and Electronic Nematicity in the Distorted Triple‐Q Magnetic Phase of Co1/3TaS2

open access: yesAdvanced Materials, EarlyView.
Co1/3TaS2${\rm Co}_{1/3}{\rm TaS}_{2}$ hosts a triple‐Q noncoplanar antiferromagnetic state with coexisting Z3${\rm Z}_3$ electronic nematicity. We report rotational hysteresis observed in both magnetoresistance and magnetic torque, revealing strongly pinned in‐plane weak ferromagnetic moments in the triple‐Q phase and the magnetism‐driven nature of ...
Joonyoung Choi   +5 more
wiley   +1 more source

Machine Learning for Parkinson’s Disease Detection: Analyzing Hybrid Voice Data With Spectral, Topological, and Random Matrix Methods

open access: yesIEEE Open Journal of the Computer Society
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects both motor and speech functions. Advances in machine learning and signal processing have enabled non-invasive PD detection through voice analysis.
Andy Dominguez-Monterroza   +2 more
doaj   +1 more source

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