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 ...

Advancements in Machine Learning for Materials Science Machine learning (ML) is really shaking things up in materials ...

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...

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

Material science, at its core, is an interdisciplinary field focusing on the discovery and design of new materials. It combines elements of physics, chemistry and engineering to understand and ...

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A new study ...

Researchers have used machine learning to create a model that simulates reactive processes in organic materials and conditions. Researchers from Carnegie Mellon University and Los Alamos National ...

An integrated framework combining first-principles calculations and machine learning was developed to predict gas-sensing performance. Key descriptors such as adsorption energy, adsorption distance, ...