Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation Article Swipe

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Yunyang Li , Yusong Wang , Lin Huang , Han Yang , Xinran Wei , Jia Zhang , Tong Wang , Zun Wang , Bin Shao , Tie‐Yan Liu ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2304.13542

Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods. However, the two approaches fail to give a satisfactory description of long-range and many-body interactions, respectively. Inspired by fragmentation-based methods, we propose the Long-Short-Range Message-Passing (LSR-MP) framework as a generalization of the existing equivariant graph neural networks (EGNNs) with the intent to incorporate long-range interactions efficiently and effectively. We apply the LSR-MP framework to the recently proposed ViSNet and demonstrate the state-of-the-art results with up to 40% MAE reduction for molecules in MD22 and Chignolin datasets. Consistent improvements to various EGNNs will also be discussed to illustrate the general applicability and robustness of our LSR-MP framework. The code for our experiments and trained model weights could be found at https://github.com/liyy2/LSR-MP.

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Computer Science

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Range (Aeronautics)

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Scalability Computer science Robustness (evolution) Message passing Theoretical computer science Fragmentation (computing) Graph Range (aeronautics) Distributed computing Artificial intelligence Machine learning Chemistry Database Biochemistry Materials science Composite material Gene Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2304.13542
PDF: https://arxiv.org/pdf/2304.13542
OA Status: green
Cited By: 6
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2304.13542

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Title: Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-04-26

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Authors: Yunyang Li, Yusong Wang, Lin Huang, Han Yang, Xinran Wei, Jia Zhang, Tong Wang, Zun Wang, Bin Shao, Tie‐Yan Liu

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Landing page: https://arxiv.org/abs/2304.13542

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Concepts: Scalability, Computer science, Robustness (evolution), Message passing, Theoretical computer science, Fragmentation (computing), Graph, Range (aeronautics), Distributed computing, Artificial intelligence, Machine learning, Chemistry, Database, Biochemistry, Materials science, Composite material, Gene, Operating system

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Cited by: 6

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abstract_inverted_index.Computational	0
abstract_inverted_index.applicability	120
abstract_inverted_index.interactions,	45
abstract_inverted_index.respectively.	46
abstract_inverted_index.generalization	60
abstract_inverted_index.Message-Passing	55
abstract_inverted_index.Long-Short-Range	54
abstract_inverted_index.state-of-the-art	92
abstract_inverted_index.fragmentation-based	29, 49
abstract_inverted_index.https://github.com/liyy2/LSR-MP.	140
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	10
citation_normalized_percentile