Klara Bonneau, Aldo S. Pasos‐Trejo, Michael Plainer, Luca Sagresti, Jacopo Venturin, Iryna Zaporozhets, Alessandro Caruso, Edoardo Rolando, Andrea Guljas, Leon Klein, Maximilian Schebek, Filippo Albani, Raquel López‐Ríos de Castro, Zakariya El Machachi, Lorenzo Giambagli, Cecilia Clementi
ABSTRACT Molecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Mechanics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods