Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...

Dhruv Shenai investigates how machine learning and lab automation are transforming materials science at Cambridge ...

A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

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

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

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

In materials science, substances are often classified based on defining factors such as their elemental composition or crystalline structure. This classification is crucial for advances in materials ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

The Materials Science Laboratory is primarily used by the Mechanical Engineering students to support relevant courses and research activities. The Material Science laboratory consists of equipment ...