Materials testing remains a cornerstone of engineering and manufacturing, ensuring that components and structures—from aircraft fuselages to bridge supports—perform reliably under demanding conditions ...

For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National ...

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

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 ...

Physics-informed machine learning connects atomic structure with ion transport and electrolyte stability, accelerating better sodium- and lithium-ion batteries.

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 ...

(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 ...