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

A systematic study finds that large language models consistently fail to learn spatial coordinate information in materials property prediction, with scaling up models or data offering no meaningful ...

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 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 ...

Material properties such as sound insulation, resistance to extreme heat and thermal expansion originate from how the zillions of microscopic building blocks (nuclei and electrons) interact at ...

This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

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

Hyperspectral remote sensing has transformed Earth observation by enabling material- and object-level identification based on diagnostic spectral ...

Self-supervised learning creates training targets from the structure of unlabeled data, allowing models to learn ...