Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
Not on the map. Why?
Abstract (arXiv)
Machine learning potentials have achieved great success in accelerating atomistic simulations. Many of them relying on atom-centered local descriptors are natural for parallelization. More recent message passing neural network (MPNN) models have demonstrated their superior accuracy and become increasingly popular. However, efficiently parallelizing MPNN models across multiple nodes remains challenging, limiting their practical applications in large-scale simulations. Here, we propose an efficient parallel algorithm for MPNN models, in which additional data communication is minimized among local atoms only in each MP layer without redundant computation, thus scaling linearly with the layer number. Integrated with our recursively embedded atom neural network model, this algorithm demonstrates excellent strong scaling and weak scaling behaviors in several benchmark systems. This approach enables massive molecular dynamics simulations on MPNN models as fast as on strictly local models for over 100 million atoms, vastly extending the applicability of the MPNN potential to an unprecedented scale. This general parallelization framework can empower various MPNN models to efficiently simulate very large and complex systems.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 70 references that are in the map. First posted on arXiv 10 May 2025.
Builds on
12 of 70 references.
References · 12
- 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
- 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
- 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
- 2020Machine Learning Force Fieldscited by 207
- 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 182
- 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
- 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
- 2023Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizecited by 33
- 2024Cartesian atomic cluster expansion for machine learning interatomic potentialscited by 31
- 202086 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracycited by 15
- 2021Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentialscited by 15
- 2020Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamicscited by 10
Built on by
Citing papers per year: 2025: 1.
1 paper.