Molecular dynamics (MD) simulations are essential tools for investigating large-scale molecular systems, yet achieving high performance and scalability on CPU-based architectures remains challenging. In this study, we present a highly optimized framework based on DeepMD-kit for conducting 500 millionatom MD simulations on an ARMv8 SVE high-performance computing (HPC) system. Key optimizations include leveraging OpenMP for multi-threaded acceleration of DeepMD-kit and utilizing the ARMv8 SVE instruction set to optimize doubleprecision matrix multiplication in PyTorch. These enhancements enable single ARMv8 SVE 64-core processors to achieve 1.3x the training performance of NVIDIA V100 GPU, and two ARMv8 SVE 64-core processors to achieve 1.05x the inference performance of NVIDIA V100 GPU. Leveraging this optimized framework, we achieve large-scale MD simulations across 4,096 computing nodes.

High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 -- 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an unprecedented 17 billion atoms. The strong scaling of a 13.5-million atom copper system shows that the time-to-solution can be 7 times faster, reaching 11.2 nanoseconds per day. This work opens the door for unprecedentedly large-scale molecular dynamics simulations based on ab initio accuracy and can be potentially utilized in studying more realistic applications such as mechanical properties of metals, semiconductor devices, batteries, etc. The optimization techniques detailed in this paper also provide insight for relevant high-performance computing applications.

For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learning based simulation protocol (Deep Potential Molecular Dynamics), while retaining ab initio accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining 91 PFLOPS in double precision (45.5% of the peak) and 162/275 PFLOPS in mixed-single/half precision. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.