Results 161 to 170 of about 229,000 (311)
Advanced Materials, EarlyView.The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley +1 more source
Diversified Adaptive Stock Selection Using Continual Graph Learning and Ensemble Approach
IEEE AccessStock selection is essential for portfolio diversification to reduce risks and maximize profits. However, stock selection is challenging owing to the non-stationary nature of stock markets.
Jae-Seung Kim, Sang-Ho Kim, Ki-Hoon Lee
doaj +1 more source
Deep Lagrangian Propagation in Graph Neural Networks [PDF]
, 2020 Graph Neural Networks (Scarselli et al., 2009) exploit an iterative diffusion procedure to compute the node states as the fixed point of the trainable state transition function. In this paper, we show how to cast this scheme as a constrained optimization
Marco Maggini +3 more
core
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.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
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
Advanced Materials, EarlyView.We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source
D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
Applied SciencesThe booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the
Zhenhua Li +4 more
doaj +1 more source
Understanding Operando Water Management in Hydroxide‐Exchange‐Membrane Fuel Cells
Advanced Materials Interfaces, EarlyView.Effective water management is vital for high‐performance hydroxide‐exchange‐membrane fuel cells. Using a custom water‐flux station, this study quantifies how membrane thickness, microporous layers, and operating conditions dictate internal water transport.
Catherine M. Weiss +4 more
wiley +1 more source
Spectral Geometry for Structural Pattern Recognition [PDF]
, 2010 Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis
El Ghawalby, Heyayda +1 more
core
Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classification [PDF]
Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven data. Specifically in signed and weighted graph structures. This makes classification tasks less effective.
Fofanah, Abdul Joseph +3 more
core +1 more source