Results 91 to 100 of about 6,810,610 (247)
Advanced Engineering Materials, EarlyView.This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
A knowledge graph and contrastive learning framework for evaluating digital economy development
Discover Artificial IntelligenceThis study proposes a digital economic development evaluation algorithm that integrates knowledge graphs and contrastive learning. By constructing a digital economic knowledge graph containing 120,000 entities and 650,000 relationships, the ...
Chengxia Li
doaj +1 more source
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
, 2023 Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction.
Yiu, Siuming +5 more
core
Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System
Advanced Engineering Materials, EarlyView.Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner +7 more
wiley +1 more source
Contrastive learning for traffic flow forecasting based on multi graph convolution network
IET Intelligent Transport SystemsContrastive learning is an increasingly important research direction and has attracted considerable attention in the field of computer vision. It can greatly improve the representativeness of image features through data augmentation, unsupervised ...
Kan Guo +7 more
doaj +1 more source
, 2023 Heterogeneous graph is a natural way to model complex relationships and interactions among entities in the real world, such as social networks or user--product relations.
Tran, Nguyen Manh Thien
core
Supporting AI Readiness Through Digital Workflows in Materials Science
Advanced Engineering Materials, EarlyView.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
Explore contrastive learning on graph representation learning
, 2022 Graph is a type of structured data to describe the multiple objects as well as their relationships, and is attracting increasing attention in recent years as it can represent various types of data structures in the real life, such as the social networks,
Liu, Qiuyu
core
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
Advanced Engineering Materials, EarlyView.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
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source