Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

However, most existing studies focus on using the available local data to jointly build prediction models, facing data security challenges and time complexity, especially with multi-dimensional ...

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided ...

We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP)─the problem of identifying the stable crystal structures that will form from a given molecule based ...

Microsoft researchers believe they have found a way to use machine learning to push those limits for small molecules (arXiv 2025, DOI: 10.48550/arXiv.2506.14665).

A new machine learning approach that draws inspiration from the way the human brain seems to model and learn about the world has proven capable of mastering a number of simple video games with ...

However, some teachers don’t incorporate chess into the classroom because they feel intimidated by the game and haven’t been shown how to use it as a tool, both experts, Root and Nash, said.

More information: Zhenghao Yin et al, Experimental quantum-enhanced kernel-based machine learning on a photonic processor, Nature Photonics (2025). DOI: 10.1038/s41566-025-01682-5 ...

In recent years, machine learning (ML) has emerged as a powerful tool for addressing complex problems across various scientific domains, including petroleum engineering. The ability of ML algorithms ...