Results 101 to 110 of about 91,361 (227)

Exceptional Antimodes in Multi‐Drive Cavity Magnonics

open access: yesAdvanced Electronic Materials, EarlyView.
Driven‐dissipative cavity‐magnonics provides a flexible platform for engineering non‐Hermitian physics such as exceptional points. Here, using a four‐port, three‐mode system with controllable microwave interference, antimodes and coherent perfect extinction (CPE) are realized, enabling active tuning to antimode exceptional points.
Mawgan A. Smith   +4 more
wiley   +1 more source

Extrinsic and Intrinsic Charge Transfer at Interfaces of Membrane‐Based Oxide Heterostructures

open access: yesAdvanced Electronic Materials, EarlyView.
Freestanding oxides have emerged as a new opportunity to tailor oxides outside of the typical epitaxial constraints. We present the fabrication of TiO2‐terminated SrTiO3 membranes via direct growth control. We demonstrate competing ionic and electronic charge transfer in LaAlO3/SrTiO3 bilayers using near ambient pressure XPS.
Kapil Nayak   +8 more
wiley   +1 more source

Uncovering the Role of Intrinsic Magnetic Order in Oxygen Evolution Reaction Activity With Operando Spectroscopy

open access: yesAdvanced Energy Materials, EarlyView.
Operando magnetic spectroscopy provides insight into how intrinsic magnetic order modulates the oxygen evolution reaction on La0.67Sr${\rm La}_{0.67}{\rm Sr}$0.33MnO$_{0.33}{\rm MnO}$3$_3$ thin films. Temperature‐dependent operando FMR and ambient‐pressure XMCD suggest a correlation between activity and the emergence of ferromagnetic regions near and ...
Emma van der Minne   +9 more
wiley   +1 more source

Operando X‐Ray Diffraction and Total Scattering Characterization of Battery Materials: Not Just a Pretty Picture

open access: yesAdvanced Energy Materials, EarlyView.
This review focuses on operando studies of battery materials by X‐ray diffraction (XRD) and total X‐ray scattering (TXS). This work highlights potential pitfalls and identify best‐practices for operando studies and reviews some unusual experiments to illustrate how these methods can be applied beyond the evaluation of the early‐stage cycling mechanisms
Amalie Skurtveit   +5 more
wiley   +1 more source

CFD modeling and sensitivity‐guided design of silicon filament CVD reactors

open access: yesAIChE Journal, EarlyView.
Abstract Filament‐based chemical vapor deposition (CVD) for silicon (Si) coatings is often treated as an adaptation of planar deposition. But this overlooks fundamental shifts in transport phenomena and reaction kinetics. In filament CVD, the filament acts as a substrate, heat source, and flow disruptor simultaneously. In this work, we ask: What really
G. P. Gakis   +8 more
wiley   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

open access: yesAdvanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows   +7 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy   +8 more
wiley   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

open access: yesAdvanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang   +4 more
wiley   +1 more source

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