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Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

Engineering and Technology

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

P. Mo, C. Li, et al.

This innovative paper unveils a breakthrough molecular dynamics methodology that blends the precision of ab initio methods with the speed of classical techniques, leveraging deep neural networks to optimize potential energy surfaces. Conducted by Pinghui Mo, Chang Li, Dan Zhao, Yujia Zhang, Mengchao Shi, Junhua Li, and Jie Liu, this research showcases transformative applications in computational simulations.

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Playback language: English
Abstract
This paper introduces a novel molecular dynamics (MD) methodology that combines the accuracy of ab initio MD (AIMD) with the efficiency of classical MD (CMD). This is achieved by using deep neural networks (DNNs) to accurately fit the potential energy surface (PES) and deploying a multiplication-less DNN on a non von Neumann (NvN) architecture to overcome the memory wall bottleneck. Testing across various molecules and bulk systems demonstrates the general applicability of this approach, which is implemented on an FPGA-based server.
Publisher
npj Computational Materials
Published On
May 09, 2022
Authors
Pinghui Mo, Chang Li, Dan Zhao, Yujia Zhang, Mengchao Shi, Junhua Li, Jie Liu
Tags
molecular dynamics
ab initio
deep neural networks
potential energy surface
non von Neumann architecture
FPGA
computational efficiency

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