Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience

2025-07-09

AI Summary

Multi-scale molecular dynamics (MD) simulations—spanning nanosecond-to-millisecond timescales—face significant challenges in orchestrating thousands of heterogeneous tasks under resource constraints (e.g., small-scale HPC clusters or laptops), hindering studies of complex biomolecular processes such as RAS–RAF membrane interactions. To address this, we propose *mini-MuMMI*, a lightweight, multi-scale workflow system. It integrates machine learning–driven dynamic resource scheduling with cross-scale coupling mechanisms, built atop a distributed workflow framework for efficient parallel orchestration. Crucially, *mini-MuMMI* enables large-scale, heterogeneous MD simulations using only limited computational resources. Experimental evaluation on the RAS–RAF system demonstrates its feasibility, strong scalability, and substantial reduction in the barrier to multi-scale modeling. Moreover, its modular design ensures broad applicability to other multi-scale scientific computing domains.

Technology Category

• Machine Learning: Scalability of ML Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scientists often rely on multiscale models comprised of several internal models operating at difference scales, ranging from microscopic to macroscopic length and time scales. Bridging the gap between different time and length scales has historically been challenging but the advent of newer machine learning (ML) approaches has shown promise for tackling that task. Multiscale models require massive amounts of computational power and a powerful workflow management system. Orchestrating ML-driven multiscale studies on parallel systems with thousands of nodes is challenging, the workflow must schedule, allocate and control thousands of simulations operating at different scales. Here, we discuss the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a multiscale workflow management infrastructure, that can orchestrate thousands of molecular dynamics (MD) simulations operating at different timescales, spanning from millisecond to nanosecond. More specifically, we introduce a novel version of MuMMI called "mini-MuMMI". Mini-MuMMI is a curated version of MuMMI designed to run on modest HPC systems or even laptops whereas MuMMI requires larger HPC systems. We demonstrate mini-MuMMI utility by exploring RAS-RAF membrane interactions and discuss the different challenges behind the generalization of multiscale workflows and how mini-MuMMI can be leveraged to target a broader range of applications outside of MD and RAS-RAF interactions.

Problem

• Bridging multiscale gaps in biomolecular modeling with ML
• Managing large-scale parallel multiscale MD simulations efficiently
• Enabling multiscale workflows on modest HPC systems via mini-MuMMI

Innovation

• Machine Learning-driven multiscale modeling workflow
• Mini-MuMMI for modest HPC systems or laptops
• Orchestrates thousands of multi-timescale MD simulations