Results 61 to 70 of about 1,286,262 (236)
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Machine learning and conventional statistics: making sense of the differences
Christophe Ley +5 more
semanticscholar +1 more source
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Machine Learning in Official Statistics
In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Beck, Martin +2 more
openaire +2 more sources
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak +14 more
wiley +1 more source
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy +3 more
wiley +1 more source
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source
Bio‐Inspired Molecular Events in Poly(Ionic Liquids)
Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley +1 more source

