Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The {\mu}m mesoscale is also the size which can be observed directly under an optical microscope, bridging the atomistic microscopic description with the continuous model macroscopic world. In this work, we report an unprecedented molecular dynamics (MD) simulation comprising 1.62 trillion atoms. Utilizing the neuroevolution potential (NEP) framework, we attained ab initio accuracy on China's New-generation Intelligent Supercomputer. Our implementation achieves a time-to-solution (s/step/atom) 100 times faster than previous state-of-the-art machine learning force field simulations, and 1,000 times faster than the Gordon Bell Prize-winning application from six years ago. Furthermore, we demonstrate an 86.9% weak scaling efficiency from a single GPGPU to 45,000 GPGPUs. These results redefine atomistic simulation boundaries, enabling direct mesoscopic modeling with quantum-level precision.
@article{arxiv.2604.24816,
title = {Trillion-atom molecular dynamics simulations with ab initio accuracy},
author = {Pengfei Suo and Wudi Cao and Xingxing Wu and Wenjie Zhang and Zheyong Fan and Shuanghan Xian and Rui Wang and Cheng Qian and Chao Liang and Qinghong Yuan and Xiaoshuang Chen and Pengfei Guan and Jingde Bu and Hongzhen Tian and Yanjing Su and Feng Ding and Lin-Wang Wang},
journal= {arXiv preprint arXiv:2604.24816},
year = {2026}
}