Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space

Understand

Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency.

• We present a novel neural network architecture, MDtrajNet, and a pre-trained foundational model, MDtrajNet-1, that directly generates MD trajectories across chemical space, bypassing force calculations and integration.
• MDtrajNet combines equivariant neural networks with a transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories.
• This approach accelerates simulations by up to two orders of magnitude and yields better accuracy than MD propagated with established machine-learning interatomic potentials trained on the same data.