Trajectory forecasting lifts the femtosecond ceiling in molecular simulation

TL;DR - A deep learning method extends molecular dynamics simulations beyond femtosecond-scale limits while retaining accurate physical-property predictions. This could enable efficient modeling of slower molecular processes.

• Uses trajectory forecasting to extend molecular dynamics time scales.
• Applies deep learning to predict molecular evolution.
• Aims to preserve physical-property accuracy over longer simulations.
• Specific benchmarks and quantitative gains are not provided in the excerpt.