NVIDIA says Tensor Core FP32 methods can accelerate PET molecular dynamics while keeping energy, force and relaxation ...

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 ...

A Columbia-led team has introduced a physics-aware benchmark showing that machine learning models can predict thermal ...

A unified atomistic study shows the partial separation model for stacking fault energy agrees with planar-defect methods for ...

Combining electronic structure calculations and machine learning (ML) techniques has become a common approach in the atomistic modeling of matter. Using the two techniques together has allowed ...

AI research can run into a physical limit long before it runs out of ideas: the amount of computing a laboratory can power ...