Researchers have introduced ChemGraph, an AI-powered agentic framework that automates and streamlines computational chemistry and materials science workflows. Combining graph neural networks for ...

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 ...

A World Bank study introduces an AI-based method using graph neural networks to break down national statistics like GDP into ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...

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

From smarter chatbots to scientific breakthroughs, combining graphs with AI is unlocking new levels of reasoning and accuracy. By structuring data into nodes and relationships, these systems gain ...

Generative AI can augment chemometrics by automating curation, connecting analytical outputs to textual knowledge, and improving interpretability for complex multivariate datasets.

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 ...