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

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 ...

For thousands of these materials, X-ray diffraction patterns exist but remain unsolved. To try to crack the structures of these materials, Freedman and her colleagues trained a machine-learning model ...

(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 ...

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 ...

The field of industrial production is increasingly dependent on materials whose origins and behaviors are intricately tied to geological processes and ...

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 ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.