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 Google DeepMind, BIFOLD, and TU Berlin have unveiled AI models that simulate molecular behavior without hard-coded physical laws, achieving competitive results through massive ...

Ra’anana, Israel, Feb. 05, 2026 (GLOBE NEWSWIRE) -- Rail Vision Ltd. (RVSN) (“Rail Vision” or the “Company”), an early commercialization stage technology company seeking to revolutionize railway ...

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.

The addition of flexural stiffeners on the transformer cover plates was explored as a means to stiffen the base of the bushings and mitigate their seismic vulnerability. Numerical and experimental ...