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

The integration of machine learning techniques into microstructure design and the prediction of material properties has ushered in a transformative era for materials science. By leveraging advanced ...

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 ...

In the age of digital transformation, machine learning (ML) is rapidly becoming a pivotal technology in various sectors. One of its most exciting applications is in the field of advanced materials ...

Deep learning, a multifaceted and groundbreaking subset of Artificial Intelligence (AI), is reshaping various sectors, notably materials science. Its algorithms are now leveraged to predict and ...

Data-driven science represents a transformative paradigm in materials science. Both data-driven materials science and informatics encompass systematic knowledge extraction from materials datasets.

A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of ...