Results 241 to 250 of about 1,029,186 (289)
Refractory high‐entropy alloys have attracted substantial attention for future ultrahigh‐temperature structural applications. Here the progression of key phase transformations in AlMo0.5NbTa0.5TiZr is established and correlated to the mechanical performance, highlighting the importance of the B2 phase.
George J. Wise+5 more
wiley +1 more source
Compensation for the source drift of a free-electron laser beamline by adjusting the fixed-focus constant of the grating monochromator. [PDF]
Xue C, Xue L, Wang Y, Tai R.
europepmc +1 more source
Conceptual design of a 15-TW pulsed-power accelerator for high-energy-density—physics experiments
R. B. Spielman+8 more
openalex +1 more source
The fabrication and post‐treatment via solvent annealing of poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate‐based electrodes using spray deposition in a roll‐to‐roll setup are presented. The decrease in sheet resistance and its correlation with nanostructure and molecular structure in the electrodes as a function of the processing parameters is ...
Marie Betker+10 more
wiley +1 more source
Publisher Correction: Uniaxial strain tuning of charge modulation and singularity in a kagome superconductor. [PDF]
Lin C+19 more
europepmc +1 more source
Machine Learning‐Guided Discovery of Factors Governing Deformation Twinning in Mg–Y Alloys
This study uses interpretable machine learning to identify key microstructural and processing parameters related to twinning in magnesium‐yttrium (Mg–Y) alloys. It is identified that using only grain size, grain orientation, and total applied strain, grains can be classified with 84% accuracy based on whether the grain contains a twin.
Peter Mastracco+8 more
wiley +1 more source
Ultra-high dose rate radiotherapy overcomes radioresistance in head and neck squamous cell carcinoma. [PDF]
Li HS+20 more
europepmc +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani+4 more
wiley +1 more source
Conditional guided generative diffusion for particle accelerator beam diagnostics. [PDF]
Scheinker A.
europepmc +1 more source