# Machine Learning for Molecular Dynamics on Long Timescales

## TL;DR

This paper reviews how machine learning techniques can enhance molecular dynamics simulations by enabling efficient long-timescale analysis, offering new models and methods to overcome computational challenges.

## Contribution

It defines key learning problems in long-timescale MD, reviews successful ML approaches, and highlights unsolved challenges at the intersection of MD and ML research.

## Key findings

- ML improves long-time MD simulations efficiency
- Successful ML models for MD dynamics are identified
- Open research problems in ML for MD are outlined

## Abstract

Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measurements, designing drug molecules, or optimizing materials, rely on statistical quantities, which may be prohibitively expensive to compute from direct long-time MD simulations. Classical Machine Learning (ML) techniques have already had a profound impact on the field, especially for learning low-dimensional models of the long-time dynamics and for devising more efficient sampling schemes for computing long-time statistics. Novel ML methods have the potential to revolutionize long-timescale MD and to obtain interpretable models. ML concepts such as statistical estimator theory, end-to-end learning, representation learning and active learning are highly interesting for the MD researcher and will help to develop new solutions to hard MD problems. With the aim of better connecting the MD and ML research areas and spawning new research on this interface, we define the learning problems in long-timescale MD, present successful approaches and outline some of the unsolved ML problems in this application field.