Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
This work addresses the problem of limited simulation timescales and system sizes in molecular dynamics for materials science and physics, enabling more efficient large-scale simulations, though it appears incremental as it builds on existing machine learning interatomic potentials.
The paper tackles the computational cost and time-step limitations of molecular dynamics simulations by introducing TrajCast, an autoregressive equivariant network framework that directly updates atomic positions and velocities, achieving forecast intervals up to 30 times larger than traditional methods and generating over 15 ns of trajectory data per day for systems with over 4,000 atoms.
Molecular dynamics (MD) simulations play a crucial role in scientific research. Yet their computational cost often limits the timescales and system sizes that can be explored. Most data-driven efforts have been focused on reducing the computational cost of accurate interatomic forces required for solving the equations of motion. Despite their success, however, these machine learning interatomic potentials (MLIPs) are still bound to small time-steps. In this work, we introduce TrajCast, a transferable and data-efficient framework based on autoregressive equivariant message passing networks that directly updates atomic positions and velocities lifting the constraints imposed by traditional numerical integration. We benchmark our framework across various systems, including a small molecule, crystalline material, and bulk liquid, demonstrating excellent agreement with reference MD simulations for structural, dynamical, and energetic properties. Depending on the system, TrajCast allows for forecast intervals up to $30\times$ larger than traditional MD time-steps, generating over 15 ns of trajectory data per day for a solid with more than 4,000 atoms. By enabling efficient large-scale simulations over extended timescales, TrajCast can accelerate materials discovery and explore physical phenomena beyond the reach of traditional simulations and experiments. An open-source implementation of TrajCast is accessible under https://github.com/IBM/trajcast.