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 ...

illustrating the comprehensive zero-shot benchmark of 19 universal machine learning interatomic potentials and the dominant impact of training data composition for surface energy prediction.

Two curated guides review 67 free AI and computational tools for your daily work in the lab, from literature discovery and academic writing to DFT and ML potentials.

The global market for 2D materials — already estimated at several billion dollars annually — is growing at a 4 percent rate. This is explained by the importance of these newly synthesized materials, ...