Unlocking exascale simulations of matter with Equivariant deep-learning accuracy

Thursday - May 25 2023, 06:41 UTC - 1 year ago

This study introduces a state-of-the-art equivariant deep learning system designed to simulate dynamics of matter at the atomic scale.The system achieves an unprecedented level of accuracy and scalability demonstrated with weak and strong scalability up to 1280 nodes and 100 million atoms, respectively.This system bridges the gap between computationally expensive electronic structure methods and uncontrolled approximations by combining machine learning and physical-based models.

This is the first scalable, transferable machine-learning potential with state-of-the-art equivariant deep-learning accuracy. Performance of 100 timesteps/s for range of biomolecular systems. 70% weak scaling to 1280 nodes and 5120 A100 GPUs, excellent strong scaling up to 100 million atoms. First application of state-of-the-art machine learning interatomic potentials to large-scale biomolecular simulations .

Problem Overview: First-Principles Dynamics of Matter The ability to predict the time evolution of matter on the atomic scale is the foundation of modern computational biology, chemistry, and materials engineering. Even as quantum mechanics governs the microscopic atom-electron interactions in vibrations, migration and bond dissociation, phenomena governing observable physical and chemical processes often occur at much larger length- and longer time-scales than those of atomic motion .

Bridging these scales requires both innovation in fast and highly accurate computational approaches capturing the quantum interactions and in extremely parallelizable architectures able to access exascale computers. Presently, realistic physical and chemical systems are far more structurally complex than what computational methods are capable of investigating, and their observable evolution is beyond the timescales of atomistic simulations .

This gap between key fundamental questions and phenomena that can be effectively modeled has persisted for decades. From one side of the gap, models of small size, representing ostensibly important parts of the systems, can be constructed and investigated with highfidelity computationally expensive models, such as electronic structure methods of density functional theory (DFT) and wave-function quantum chemistry .

In the domain of materials science, these models can capture individual interfaces in metallic composites, defects in semiconductors, and flat surfaces of catalysts. However, evolution of such structures over relevant time scales is out of reach with electronic structure methods. Importantly, such reduction of complexity is not possible in the domain of biological sciences, where entire structures of viruses consist of millions of atoms, in addition to similarly large number of explicit water molecules needed to capture the physiological environment .

From the other side of the gap, uncontrolled approximations have to be made to reach large sizes and sufficient computational speeds. These approximations have relied on very simple analytical models for interatomic interactions and have many documented failures of describing dynamics of both complex inorganic and biological materials. Molecular dynamics (MD) simulations are a pillar of computational science, enabling insights into the dynamics of molecules and materials at the atomic scale .

MD provides a level of resolution, understanding, and control that experiments often cannot provide, thereby serving as an extremely powerful tool to advance our unstanding and design of novel molecules and materials. Molecular dynamics simulates the time evolution of atoms according to Newton’s equations of motion. By integrating the forces due to interatomic interactions, the solution of these equations provides full information about the trajectories of individual atoms and molecules in the system, allowing full realization of the underlying atomic-scale dynamics .