Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.

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.

The atomic kinetic Monte Carlo method plays an important role in material simulations by connecting the microscale mechanism with macroscale evolution. However, the long-time simulation of multi-component materials is highly challenging because it demands significant computing resources. With the advent of exascale computing, ultra-high computing power can enable kinetic Monte Carlo (KMC) simulations. In this paper, we deeply optimize OpenKMC for the new-generation Sunway supercomputer. This includes optimizing the memory access for the SW39000 architecture, eliminating various redundant computations at growing scales, and proposing a communication strategy for heterogeneous platforms. In addition, we expanded OpenKMC's simulation for multi-component alloys. Finally, the acceleration framework can produces a$37\times$performance enhancement on the Sunway platform. Furthermore, when powered by 10 million cores, our program can perform trillion-atom simulations of complex multi-component alloys with 85% parallel efficiency.

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.