Results 121 to 130 of about 7,645,086 (295)
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Locally Adaptive Translation for Knowledge Graph Embedding
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss
Wang, Yuanzhuo +4 more
core +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
ShallowBKGC: a BERT-enhanced shallow neural network model for knowledge graph completion [PDF]
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. One of the effective ways for knowledge graph completion is knowledge graph embedding.
Ningning Jia, Cuiyou Yao
doaj +2 more sources
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated.
Bianchi Federico +4 more
openaire +4 more sources
Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models
Leveraging literature data, we build a large‐language‐model‐driven workflow that extracts synthesis steps from 4407 papers, retrieves similar precedents, and generates candidate solid‐state synthesis recipes. The system benchmarks against ground‐truth and then operates in a closed loop with experiments to synthesize oxy‐selenide electrolyte materials ...
Dong Won Jeon +9 more
wiley +1 more source
We mechanically program liquid crystal elastomer coatings as a stimuli‐responsive and digitally addressable platform for refreshable tactile displays; we validate the perceptual performance of our dynamic device matches conventional static tactile media.
Tom Bruining +9 more
wiley +1 more source
Graph Simultaneous Embedding Tool, GraphSET
Problems in simultaneous graph drawing involve the layout of several graphs on a shared vertex set. This paper describes a Graph Simultaneous Embedding Tool, GraphSET, designed to allow the investigation of a wide range of embedding problems.
Alejandro Estrella-Balderrama +5 more
core +1 more source
A survey of two-dimensional graph layout techniques for information visualisation [PDF]
Many algorithms for graph layout have been devised over the last 30 years spanning both the graph drawing and information visualisation communities. This article first reviews the advances made in the field of graph drawing that have then often been ...
Vickers, Paul, Gibson, Helen, Faith, Joe
core +1 more source
Micro‐topographical cues applied through temporally controlled microscale confinement improve the reproducibility, spatial organization, and neurosensory‐associated features of pluripotent stem cell‐derived inner ear organoids. Integration with a vascularized organoid platform further enables controlled investigation of vascular‐epithelial interactions
Harshita Sharma +15 more
wiley +1 more source

