Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...

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 ...

Harvard researchers bring the accuracy, sample efficiency, and robustness of deep equivariant neural networks to the simulate 44 million atoms. This is achieved through a combination of innovative ...

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, ...