Results 191 to 200 of about 1,366,171 (283)
Coupling mechanism of dynamic incidence angle and multipulse accumulation in femtosecond laser ablation of complex spatial surfaces. [PDF]
Wang R, Hu Y, Ming X, Yue Q, Li W.
europepmc +1 more source
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen +7 more
wiley +1 more source
Mechanisms and advances in field assisted machining of SiC. [PDF]
Wang P +7 more
europepmc +1 more source
Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh +8 more
wiley +1 more source
Laser-Assisted Diamond Cutting for Low-Damage Fabrication of High-Q CaF<sub>2</sub> Whispering-Gallery Mode Resonators. [PDF]
Yang R +8 more
europepmc +1 more source
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su +13 more
wiley +1 more source
Confined shear-induced structure discretization enables re-entrant super-repellent metals. [PDF]
Wang X +12 more
europepmc +1 more source
ULTRA PRECISION MACHINE TOOL ELEMENTS AND MACHINING PROCESSES. [PDF]
openaire +2 more sources
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source

