Results 141 to 150 of about 3,278,052 (298)

Towards the Application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets [PDF]

Backpropagation-Free Graph Convolutional Networks (BFGCN) are backpropagation-free neural models dealing with graph data based on Gated Linear Networks.
Nicolo Navarin, Luca Pasa, Alessandro Sperduti   +2 more
core   +1 more source

Atomistic Kinetics of Dislocation‐Mediated Grain Growth in Monolayer MoS2

Advanced Science, EarlyView.
Atomic‐resolution in‐situ heating microscopy directly visualizes dislocation‐mediated grain boundary migration and grain growth in monolayer MoS2. Mobile grain boundaries migrate through collective motion of Mo 5|7 dislocations, whereas S 5|7 defects remain largely immobile.
Chang‐Won Choi, Min‐Yeong Choi, Tao Du, Jinho Rhee, Seokho Moon, Yeon‐Seo Nam, Xiaoyan Lu, Jong Kyu Kim, Jungwon Park, Moon‐Ho Jo, Cheol‐Joo Kim, Si‐Young Choi   +11 more
wiley   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Social Media Analytics with Graph Convolutional Networks [PDF]

, 2020
Online social media, such as Facebook, has become a norm in our social and personal lives. The explicit or implicit social relationships established on social networks can be leveraged to market products or make recommendations.
Lin, Wanyu
core  

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Path Connectivity Based Neighbor‑Awareness Node Classification Algorithm

Shuju Caiji Yu Chuli
Graph convolutional neural networks obtain the node representation by aggregating the neighbor node information with high similarity，and selecting the appropriate neighborhood for the node and conducting effective aggregation are the keys to the graph ...
ZHENG Wenping, WANG Xiaomin, HAN Zhaorong   +2 more
doaj   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Attributed Graph Classification via Deep Graph Convolutional Neural Networks [PDF]

, 2019
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep ...
Suresh, Susha Pozhampallan
core   +1 more source