Researchers from BIFOLD and Google DeepMind have developed MD-ET, a transformer-based molecular dynamics model that achieves state-of-the-art results without encoding traditional physical constraints ...

A simple transformer-based model challenges the role of physical constraints in molecular dynamics simulations Simulating how atoms and molecules move over time is a central challenge in computational ...

Researchers from BIFOLD and Google DeepMind have developed MD-ET, a transformer-based molecular dynamics model that omits traditional physics constraints like energy conservation and equivariance.

Google DeepMind published a research paper that proposes language model called RecurrentGemma that can match or exceed the performance of transformer-based models while being more memory efficient, ...

Electrochemical impedance spectroscopy (EIS) provides valuable insights into the physical processes within batteries – but how can these measurements directly inform physics-based models? In this ...