TENSORMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential

AI has been integrated into HPC across various scientific fields, significantly enhancing performance. In molecular dynamics simulations, HPC+AI facilitates the investigation of atomic-scale physical properties using machine-learning interatomic potentials (MLIPs). However, general-purpose ML tools (e.g., TensorFlow) used in MLIPs are not optimally matched, leading to missed optimization opportunities due to the higher computational complexity and greater diversity of HPC+AI applications compared to pure AI scenarios. To address this, we introduce TensorMD, an MLIP independent of existing ML tools, enabling flexible optimizations that standard ML frameworks cannot support. TensorMD outperforms a state-of-the-art MLIP—winner of the 2020 Gordon Bell Prize and built on an ML tool—by 1.88 × on NVIDIA A100 GPU. Additionally, TensorMD was evaluated on two supercomputers with different architectures, achieving significantly reduced time-to-solution and supporting molecular dynamics simulations at scales beyond 50 billion atoms.