My project focuses on developing integrated machine learning (ML) and molecular dynamics (MD) methods to accelerate the atomistic modeling of organic redox-flow batteries. The goal is to create efficient and accurate computational tools that can quickly assess battery performance by simulating structural and chemical changes within the electrolyte, electrodes, and their interfaces. A key challenge is the treatment of long-range electrostatic interactions, which are crucial for capturing the complex behavior of these systems. The work builds on SCM’s existing infrastructure, including automated ML force field training and a flexible MD engine, and will contribute to user-friendly software that supports battery design across academia and industry.