AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...

A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the ...

人工智能正深刻重塑材料科学研发范式，在加速新材料发现与性能优化中展现出突破性价值。通过高通量计算与机器学习的深度融合，传统「试错法」存在的实验周期长、资源消耗大等痛点被有效破解， 材料探索进入到「计算驱动-实验验证」的高效迭代阶段。

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...

Abstract: With the rise of large language models in natural language processing, the use of various generative large language models for entity recognition has become a cutting-edge research approach.

Department of Chemistry and Biochemistry, University of Wisconsin─Eau Claire, Eau Claire, Wisconsin 54702, United States ...

Neural processing unts (NPUs) are the latest chips you might find in smartphones and laptops — but what are they ard why are they so important? When you purchase through links on our site, we may earn ...

Please cite the following work if you want to use CGCNN. @article{PhysRevLett.120.145301, title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material ...

Abstract: Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and ...