Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science.

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.

Shaping up: A new machine learning algorithm helps physicists reconstruct the shapes of particle accelerator beams from tiny amounts of training data. (Courtesy: Greg Steward/SLAC National Accelerator ...

Analogue interaction Illustration of how an impurity atom may gradually evolve into a quasiparticle by interacting with a surrounding medium. This mechanism is similar to how an electron can distort a ...

From ketchup to quicksand, non-Newtonian fluids have long fascinated and puzzled scientists. Unlike ordinary fluids, their ...

In Drexel University’s physics department, faculty and students work side-by-side to explore the span of universal phenomena – from biophysics to astrophysics and cosmology, all the way down to the ...