Loading [MathJax]/extensions/MathZoom.js
Ex-NNQMD: Extreme-Scale Neural Network Quantum Molecular Dynamics | IEEE Conference Publication | IEEE Xplore

Ex-NNQMD: Extreme-Scale Neural Network Quantum Molecular Dynamics


Abstract:

Deep learning is revolutionizing countless scientific and engineering fields. In particular, SC20 Gordon Bell award represented a breakthrough in molecular simulation, i....Show More

Abstract:

Deep learning is revolutionizing countless scientific and engineering fields. In particular, SC20 Gordon Bell award represented a breakthrough in molecular simulation, i.e., 100-million-atom simulation with quantum-mechanical accuracy on the Summit supercomputer at ORNL, using deep potential molecular dynamics (MD). Moving forward, while these simulations were performed only in gentle equilibrium conditions, far-from-equilibrium MD simulation involving light-induced electronic excited states finds numerous scientific and engineering applications. However, it remains a challenge to perform such far-from-equilibrium simulations at larger spatiotemporal scales, where growing number of unphysical predictions of interatomic force prohibits simulations involving larger numbers of atoms for longer times. In this paper, we propose a physically-based inductive bias, maximally-preserved Maxwell-Boltzmann (MPMB), to overcome this fidelity-scaling problem. Along with hybrid divide-and-conquer parallelization and single-node level optimization using multithreading and data parallel SIMD, the resulting Ex-NNQMD (extreme-scale neural network quantum molecular dynamics) algorithm has achieved unprecedented scales of far-from-equilibrium simulations: 1) 5.1-billion atom system with a parallel efficiency of 0.94, and 2) a sustained performance of 6.4 nanoseconds/day for 10-million atom system both on 262,144 cores of the Theta supercomputer at Argonne Leadership Computing Facility. Extended fidelity scaling and efficient parallelization have allowed us for the first time to study light-induced ferroelectric switching under extreme electronic excitation at experimentally relevant spatiotemporal scales with accuracy.
Date of Conference: 17-21 June 2021
Date Added to IEEE Xplore: 24 June 2021
ISBN Information:
Conference Location: Portland, OR, USA

Funding Agency:


I. Introduction

Last decade has witnessed a surge of machine-learning applications to model complex electronic dynamics due to underlying quantum mechanics [1]. An example is neural network quantum molecular dynamics (NNQMD), where neural network is trained to reproduce quantum-mechanically obtained atomic energy or force to perform MD simulations [2]-[6]. NNQMD has attracted great attention because of its algorithmic scalability, orders of magnitude faster time-to-solution (T2S), and quantum-mechanically accurate trajectory. SC20 has marked a milestone, demonstrating a 100-million atom NNQMD simulation on the Summit supercomputer at ORNL [7]. Such a NNQMD simulation has opened up a possibility of novel materials simulations. However, NNQMD simulations have thus far been limited to gentle equilibrium conditions, and those involving excited electrons have been hindered due to the complex energy landscape far-from-equilibrium. Such highly-nontrivial interatomic interaction suffers from uncertainty in model prediction due to unseen atomic configuration. The prediction uncertainty results in unphysical atomic force, which quickly deteriorates the fidelity of obtained atomic trajectory, or even worse, cause unpredictable termination of the simulation. The number of unphysical predictions is expected to scale with respect to the simulation size and length, therefore the scaling of simulation fidelity becomes a critical issue for large-scale NNQMD on soon arriving exascale supercomputing platforms. Conventional active learning approaches to this problem generate new training data and retrain the model when a simulation failed. This cycle of simulation failure and model rebuilding is too costly for forthcoming exascale NNQMD simulations. Another commonly used method is NVT ensemble, such as Nose-Hoover thermostat [8], to keep atomic velocity fluctuates around a specified temperature. The use of thermostat algorithm helps to regulate atomic velocities to some extent, however, it does not provide a mean to control unphysical prediction beyond a certain threshold. A potential light-overhead alternative may use inductive bias [9], [10], which is a set of assumptions for a machine learning model to predict when training data does not exist, e.g., margin maximization in support vector machine. Though a good inductive bias may substantially improve the fidelity of generalization performance, it is rarely discussed in the materials simulation context.

References

References is not available for this document.