Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning (2005.00223v3)

Abstract: For 35 years, {\it 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 {\it 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 {\it ab initio} accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.

Advances in Large-Scale Molecular Dynamics Simulations Using Machine Learning

The paper discusses a significant advancement in molecular dynamics (MD) simulations, particularly focusing on enhancing computational efficiency to reach ab initio accuracy for large-scale systems. The authors propose a machine learning-based approach employing the Deep Potential Molecular Dynamics (DeePMD) framework. This method allows for the simulation of more than 1 nanosecond-long trajectories of over 100 million atoms per day, maintaining ab initio accuracy through highly optimized code on GPUs, specifically running on the Summit supercomputer.

Computational Strategy and Achievements

Traditional ab initio molecular dynamics (AIMD) is widely utilized for modeling atomic interactions accurately by generating interatomic forces using first-principles electronic structure methods, usually involving density functional theory (DFT). However, its computational cost typically limits its application to relatively small systems, mostly up to a few thousand atoms. The presented research overcomes this limitation by integrating machine learning techniques, which significantly mitigate the cubic scaling challenges associated with electronic degrees of freedom in AIMD.

1. Deep Potential Framework: The Deep Potential model utilizes deep neural networks (DNNs) to represent potential energies, offering high-dimensional potentials with the flexibility and precision required to emulate AIMD. The model constructs comprehensive descriptors of atomic environments, encapsulating symmetry-preserving features that the DNN leverages to predict potential energy contributions efficiently.
2. Scalability and Performance: The implementation scales efficiently across the entire capacity of the Summit supercomputer, achieving up to 91 PFLOPS in double precision and up to 275 PFLOPS in mixed-half precision. These performances highlight significant improvements, demonstrating more than 1000x enhancement over previous state-of-the-art capabilities.
3. Mixed Precision Implementation: The paper explores mixed precision schemes to further boost computational efficiency without compromising the accuracy of physical properties. By using a blend of single, half, and double precision floating-point operations, the framework minimizes computational overhead while maintaining robust accuracy.

Implications and Future Directions

This development opens the possibility for detailed simulations of extensive material systems with accuracy that was previously unattainable due to computational constraints. The ability to simulate longer temporal and larger spatial scales allows researchers to explore phenomena such as crack propagation in materials, complex chemical reactions, and dynamics in biological systems with unprecedented resolution.

1. Practical Implications: The ability of this methodology to simulate large systems with high accuracy could have substantial implications for material discovery and engineering, facilitating the design of new compounds and the exploration of material behaviors under different conditions.
2. Theoretical Opportunities: From a theoretical standpoint, the integration of machine learning with traditional physics-based models represents a robust enhancement to computational physical chemistry and material science, providing insights previously limited by computational resources.
3. Future Developments in AI and HPC Integration: This work exemplifies the considerable potential in integrating machine learning with high-performance computing (HPC) platforms. Future work is likely to extend these approaches, resulting in more generalized frameworks capable of addressing a broader spectrum of scientific questions. Such progress will require advanced algorithms, improved neural network architectures, and further enhancements in computing hardware to manage and optimize massive parallelism and memory bandwidth.

In conclusion, this paper demonstrates a compelling example of leveraging machine learning to expand the capability of molecular dynamics simulations. The research illustrates an essential step towards achieving large-scale computations akin to the exascale computing horizon, which promises to revolutionize various scientific domains through extensive, precise, and rapid simulations.