Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives

Abstract Atomic-scale simulations, such as molecular dynamics (MD), have undergone a rapid evolution during the past decades, leading to their broad adoption in various scientific and engineering fields, where they have unveiled hidden phenomena otherwise inaccessible through experiments. In recent years, the integration of machine learning (ML), either as a post-processing tool or embedded directly in a hybrid manner to the simulation workflow, has further enhanced the predictive power of MD by improving accuracy, reducing computational costs, and uncovering patterns in high-dimensional data. This review investigates the integration of MD with ML by identifying existing approaches for their combination within popular simulation platforms. It incorporates an analysis of research trends from 2020 to 2025 based on Scopus-indexed publications. By synthesizing recent developments and highlighting ongoing challenges, this review aims to guide future efforts in exploiting the full potential of MD/ML integration across scientific and engineering disciplines, such as computational chemistry and physics, biomolecular engineering, medicine, soft matter physics, materials science and nanotechnology.