A Columbia-led team has introduced a physics-aware benchmark showing that machine learning models can predict thermal ...

Engineering economical and scalable processes for direct air capture (DAC) from humid air relies on atomistic modeling to search and identify promising sorbent materials from a va ...

All materials contain inherent defects. These are not always harmful, since they can be used to engineer and introduce ...

A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features ...

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, ...

Following the success of large language models, the concept of large materials models as deep-learning computational models for materials design has attracted great interest. Nevertheless, the task of ...

A machine learning model built on eight material descriptors predicted the specific capacity of nitrogen-doped porous carbon ...

A general-purpose LLM is fine-tuned with inorganic material knowledge datasets and used to predict the synthesizability and precursor compounds of hypothetical inorganic materials. Seoul National ...

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...

The proposed method combines a graph neural network with hierarchical clustering to extract key features linking crystal structure to optical spectra, and then groups materials with similar structural ...