Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD.

翻译：摘要：分子动力学模拟已成为在原子尺度（尤其在极端条件下）研究材料物理性质与动力学行为的有力工具。基于机器学习的原子间势现能够实现第一性原理精度。随着高性能计算的近期进展，高精度大规模模拟已具备可行性。本研究提出一种融合物理原理与张量图的新型机器学习原子间势模型——TensorMD。张量形式不仅提供了更高效的计算能力，还增强了与其他科学代码的兼容灵活性。此外，我们提出了多种可移植优化策略，并为新一代神威超级计算机开发了高度优化版本。优化后的TensorMD在新一代神威上实现了前所未有的性能，可模拟多达520亿个原子，单步单原子耗时仅31皮秒，创下高性能计算、人工智能与分子动力学交叉领域的新纪录。