A U-Net Enhanced Graph Neural Network to Simulate Geological Carbon Sequestration
Monitoring carbon dioxide (CO2) saturation plume movement and pressure buildup is critical for ensuring the environmental safety of geological carbon storage (GCS) projects.
Hoteit, Hussein +6 more
core +1 more source
Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery
Advanced Science, EarlyView.DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong +8 more
wiley +1 more source
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 +9 more
wiley +1 more source
Few-Shot Image Classification Algorithm of Graph Neural Network Based on Swin Transformer
In fewshot image classification tasks, capturing remote semantic information in feature extraction modules based on convolutional neural network and single measure of edgefeature similarity are challenging.
Zhang, W, Ren, J, Wang, K
core +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 +4 more
wiley +1 more source
Representing Born effective charges with equivariant graph convolutional neural networks
Scientific ReportsGraph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the ...
Alex Kutana +3 more
doaj +1 more source
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 +3 more
wiley +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 +4 more
wiley +1 more source
Graph Neural Networks vs Convolutional Neural Networks for Graph Domination Number Prediction
CoRRWe investigate machine learning approaches to approximating the \emph{domination number} of graphs, the minimum size of a dominating set. Exact computation of this parameter is NP-hard, restricting classical methods to small instances. We compare two neural paradigms: Convolutional Neural Networks (CNNs), which operate on adjacency matrix ...
Randy Davila, Beyzanur Ispir
openaire +2 more sources
GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence [PDF]
Breast cancer is a major health problem worldwide, and an accurate prediction of its recurrence is crucial to early detection of recurrence and personalized treatment.
Aleskandarany, M. +3 more
core +1 more source