Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

A universal potential for all-purpose atomic simulations has been pursued for decades, but remains challenging due to limitations in model expressiveness and dataset construction. Now, writing in the ...