Results 91 to 100 of about 3,573 (263)

Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe‐Ge‐Te Composition‐Spread Thin Film Libraries

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Magnetic 2‐dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ≈ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature.
Chih‐Yu Lee   +7 more
wiley   +1 more source

Thermodynamics of the Einstein-Maxwell system

open access: yesJournal of High Energy Physics
At first glance, thermodynamic properties of gravity with asymptotically AdS conditions and those with box boundary conditions, where the spatial section of the boundary is a sphere of finite radius, appear similar. Both exhibit a similar phase structure
Shoichiro Miyashita
doaj   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

open access: yesAdvanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan   +12 more
wiley   +1 more source

Disorder‐Driven Ionic Mobility Edge and Localization‐Driven Dendrite Formation in Solid Electrolytes

open access: yesAdvanced Energy Materials, EarlyView.
Migration‐barrier disorder localizes ionic transport into a sparse percolation backbone. Current and electric‐field focusing at filament tips promote fractal dendrite growth and suppress the critical current density according to jcrit = j0 exp(−σEeff/kBT).
Dongwook Lee, Jiwon Seo
wiley   +1 more source

PUMA: Deep Metric Imitation Learning for Stable Motion Primitives

open access: yesAdvanced Intelligent Systems
Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial ...
Rodrigo Pérez‐Dattari   +2 more
doaj   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

open access: yesAIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim   +2 more
wiley   +1 more source

Lambert’s non-Euclidean geometry [PDF]

open access: yesBulletin of the American Mathematical Society, 1893
openaire   +2 more sources

Advanced Graph Neural Networks for Smart Mining: A Systematic Literature Review of Equivariant, Topological, Symplectic, and Generative Models

open access: yesMathematics
The transition of the mining industry towards Industry 5.0 demands predictive models capable of strictly adhering to physical laws and modeling complex, non-Euclidean geometries—capabilities often lacking in standard graph neural networks.
Luis Rojas   +2 more
doaj   +1 more source

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
We discovered novel materials with giant dielectric constants by combining first‐principles phonon calculations and machine learning. Screening 525 perovskites identified six candidates. RbNbO3 was synthesized under pressure and showed ε ≈ 800–1000. This validates our framework as a powerful tool for high‐performance dielectric materials discovery.
Hiroki Moriwake   +9 more
wiley   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

open access: yesAdvanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez   +4 more
wiley   +1 more source

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