Element Materials Technology’s AI-Driven Discovery Finding new materials used to take ages. Seriously, think years of lab ...

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 ...

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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 ...

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

The team developed a computational framework using robotic path planning algorithms to rapidly identify optimal composition gradients between dissimilar materials. The framework enables the creation ...

Open Materials 2024 will be one of the biggest data sets available for materials science. Meta is releasing a massive data set and models, called Open Materials 2024, that could help scientists use AI ...

Modeling and creating simulations are key skills in any math, science or engineering profession. That’s why we’ve created a unique, interdisciplinary computational science minor. This minor gives ...

Professor James Rondinelli collaborated with IBM to help turn simulations into potential practical chip designs.

UD’s interdisciplinary graduate traineeship provides students with key communication and technical skills needed to address complex, real-world problems With over 350,000 commercially available ...