Results 161 to 170 of about 15,581 (265)
Extrinsic and Intrinsic Charge Transfer at Interfaces of Membrane‐Based Oxide Heterostructures
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
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci +6 more
wiley +1 more source
Matrix-Free Inexact Preconditioning Techniques for Isogeometric Tensor-Product Discretizations. [PDF]
Mika MŁ, Hiemstra RR, Schillinger D.
europepmc +1 more source
Scan‐Path‐ and Initial‐State‐Dependent Superdomain Switching in (111)‐Oriented PZT
Scan trajectory and initial superdomain topology govern polarization switching in (111)‐oriented PZT. Automated AFM writing, pulsing experiments, and interferometric 3D‐PFM show that raster scans reproducibly stabilize ordered Type‐I stripe superdomains with constrained variant selection, whereas spiral trajectories generate frustrated mixed‐variant ...
Rama Vasudevan +11 more
wiley +1 more source
A blockchain-based multi-authority hierarchical attribute encrypted data sharing scheme in the Internet of Medical Things. [PDF]
Yuan H, Dong G, Zhao L.
europepmc +1 more source
A Dual‐Branch Flux‐Based Extended Memristor Model With Machine‐Learning‐Assisted Calibration
Multilayer oxide memristors integrated in crossbar arrays are described through a dual‐branch, flux‐controlled compact model. A three‐stage calibration workflow combining Latin hypercube sampling, Bayesian optimization, and gradient‐based refinement extracts device parameters from experimental data.
Davide Rossetti +6 more
wiley +1 more source
Estimating the Hydrogen Bond Strength by Machine Learning Approaches. [PDF]
Samangani N, Zahn S.
europepmc +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Direct Products for the Hamiltonian Density Property. [PDF]
Andrist RB, Huang G.
europepmc +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source

