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. A ...

Molecular dynamics simulations have become an indispensable tool for understanding the fundamental interactions between atoms and molecules. By utilising carefully derived interatomic potentials, ...

An international team of researchers has developed a new method for parameterizing machine-learning interatomic potentials (MLIP) to simulate magnetic materials, making the prediction of their ...

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 ...

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, ...