Materials testing remains a cornerstone of engineering and manufacturing, ensuring that components and structures—from ...

This study used machine learning models to predict the thermal conductivity of heat-transfer materials based on steelmaking slag. A dataset containing various physical parameters of the heat-transfer ...

A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has ...

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

(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 ...

MIT researchers created a technique that captures chemical arrangements across materials to improve predictions of how metal ...

Open Materials 2024 will be one of the biggest data sets available for materials science. Meta is releasing a massive data set and models, called Open Materials 2024, that could help scientists use AI ...

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

Materials informatics applies data-driven strategies to materials R&D. Long before generative AI technology reached peak hype, it had a long history of success in this field. A common approach is to ...

Engineers now use simulations of adhesively bonded joints as a common design tool. Robust numerical simulation of adhesively bonded structures requires detailed Material Models based on solid ...