A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a ...

Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists. Their ML-based model could be ...

(a) A feasible route for developing large materials models capable of describing the structure-property relationship of materials. The universal materials model of DeepH accepts an arbitrary material ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Superconductors sit at the heart of some of the most ambitious technologies on the horizon, from lossless power grids to practical quantum computers, yet finding new ones has long been a slow, ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

The development of next-generation metallic materials is entering a transformative era driven by data-driven methodologies. Traditional trial-and-error ...