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

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 ...

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 ...

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 ...

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

A new wave of physics-informed AI is accelerating the way scientists design and understand advanced materials. By embedding physical laws into machine learning models, researchers can simulate, test, ...

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 ...