Results 61 to 70 of about 14,736 (198)
Fine‐tuning steric and C─H···π contacts directs assembly of two CuI cages. An identical naphthylene‐based subcomponent forms a [CuI12L6]12+ pseudo‐hexagonal prism stabilized by 29–32 C─H···π contacts and 12 arene stacking pairs with 6‐methyl‐2‐formylpyridine, whereas the 3‐methyl analog imparts steric clashes, yielding a [CuI8L4]8+ open prism ...
Houyang Xu +3 more
wiley +2 more sources
Molecular dynamics simulations with machine learning potentials, combined with experiments, reveal how interlayer metals govern Li alloying and crystallization in zero‐excess lithium batteries. Mg and Zn promote solid‐solution alloy‐mediated pathways that influence Li diffusion and structural uniformity, while Bi forms ordered intermetallics with more ...
Neubi F. Xavier Jr. +10 more
wiley +1 more source
Oxygen substitution in NaTaOxCl6‐2x drives structural evolution from isolated [TaCl6]– octahedra, through oxygen‐bridged [Ta2OCl10]2– dimers, toward extended trans‐[TaO2Cl4]3– chain‐like arrangements. At intermediate compositions, zero‐dimensional corner‐sharing motifs are proposed to create a flexible, disordered framework that peaks ionic ...
Justin Leifeld +17 more
wiley +1 more source
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley +1 more source
Towards Advanced Intelligent and Perceptive Soft Grippers
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim +4 more
wiley +1 more source
IDENTIFYING THE SET OF SSD-EFFICIENT MIXTURES OF RISKY ALTERNATIVES [PDF]
Target MOTAD and other direct utility-maximization models provide one way of computing SSD-efficient mixtures. These models are appropriate when the utility function is known and can also be used to identify part of the set of SSD-efficient mixtures even
Kliebenstein, James B. +1 more
core +1 more source
Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp)
Network Biology ISSN 2220-8879
networkbiology@iaees.org
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