Characterizing Machine Learning Force Fields as Emerging Molecular Dynamics Workloads on Graphics Processing Units

2026-03-04

AI Summary

This study addresses significant performance bottlenecks in machine learning force fields (MLFFs) when executing molecular dynamics simulations on GPUs, primarily caused by irregular memory access patterns, low data reuse, and inefficient kernel execution during descriptor computation and neural network inference. For the first time, the work provides a systematic architectural-level analysis of MLFF workloads and introduces a scalability evaluation framework tailored for drug discovery. It establishes a new benchmark based on controllable-scale polyacrylic acid chains and conducts fine-grained GPU performance profiling using hardware-aware analysis tools. The findings uncover key performance-limiting factors and offer critical insights to guide hardware-algorithm co-optimization strategies for accelerating MLFF-based simulations.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Molecular dynamics (MD) simulates the time evolution of atomic systems governed by interatomic forces, and the fidelity of these simulations depends critically on the underlying force model. Classical force fields (CFFs) rely on fixed functional forms fitted to experimental or theoretical data, offering computational efficiency and broad applicability but limited accuracy in chemically diverse or reactive environments. In contrast, machine learning force fields (MLFFs) deliver near quantum chemical accuracy at molecular-mechanics cost by learning interatomic interactions directly from high level electronic structure data. While MLFFs offer improved accuracy at a fraction of the cost of quantum methods, they introduce significant computational overhead, particularly in descriptor evaluation and neural network inference. These operations pose challenges for parallel hardware due to irregular memory access, minimum data reuse and inefficient kernel execution. This work investigates the hardware performance of such models using poly alanine chains, a novel benchmark molecule system(s) with controllable input size, which used as performance evaluation test cases highlighting the computational bottlenecks of the graphical processor units when scaling out MLFF simulations. The analysis identifies key bottlenecks in descriptor and force computation, memory handling, highlighting the opportunities for improvements in the emerging area of MLFF based MD in drug discovery, that has received limited attention from a computer architecture perspective.

Problem

• machine learning force fields
• molecular dynamics
• GPU performance
• computational bottlenecks
• descriptor evaluation

Innovation

• machine learning force fields
• molecular dynamics
• GPU performance
• descriptor computation
• poly alanine benchmark