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 ...

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...

Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

DeepMind has shared the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. They introduce Graph Networks for Materials Exploration (GNoME), a new deep learning ...

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 ...

You’ll tackle projects in computational materials design (from high-throughput modeling and phase-diagram simulations to training machine-learning models on experimental signals such as UV–Vis/IR) ...

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 ...

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...

Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...