Results 171 to 180 of about 845,850 (303)
We proceeded with graph embedding in two stages. In step 1, the embedding of the node is obtained using the weighted node2vec method. In step 2, final graph embedding is conducted by using the node embedding obtained in step 1 as an feature of each node ...
Soohwan Jeong (18388644) +3 more
core +1 more source
Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials
Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley +1 more source
Graph sampling for node embedding
Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations.
Zhang, Li-Chun
core
Fabrication Routes for Ionic Conducting Fiber Strain Sensors
Ionic conducting fiber strain sensors (ICFSs) offer compliant, textile‐integrable sensing. Thus far, the commercialization of ICFSs has been constrained by fiber fabrication routes. This review provides a fabrication‐centric analysis of ICFSs correlating processing strategies with material properties and scalability.
Leo John Kershaw +3 more
wiley +1 more source
Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen +14 more
wiley +1 more source
Effects of Mg on Microstructure and Solidification of a Hypereutectic Zn–8 wt.%Al Alloy
An appreciable set of results involving thermal data, microstructure, chemical composition, and microstructural growth laws is reported for ZnAlMg alloys. Such results demonstrate that ZnAlMg alloys have high potential for applications in automotive self‐lubricating components, batteries, and electrical systems.
Raí B. de Sousa +6 more
wiley +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph Mining
Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs.
Nguyen, Thanh Hung +6 more
core +1 more source
ABSTRACT Chitosan, a polysaccharide upcycled from biowaste, has long promised sustainable, biocompatible, and antimicrobial films and devices, an appeal reinforced by its recent regulatory recognition, with several chitosan‐based antibacterial dressings gaining clearance through the Food and Drug Administration's 510(k) pathway.
Jacopo Nicoletti +16 more
wiley +1 more source

