Coarse-Graining of Molecular Dynamics Using Neural Ordinary Differential Equations

• Published in: IEEE EUROCON 2023 - 20th International Conference on Smart Technologies pp. 105 - 110
• Main Authors: Lala, Jakub, Angiolleti-Uberti, Stefano
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06-07-2023
• Subjects: coarse-graining; deep learning; molecular dynamics; neural ordinary differential equations; Ordinary differential equations; Pipelines; Proteins; Quaternions; rigid body dynamics; Software; Toy manufacturing industry; Training
• DOI: 10.1109/EUROCON56442.2023.10199088

Coarse-graining (CG) improves computational feasibility of simulating complex molecular systems (e.g. proteins, or polymer chains) by reducing the number of degrees of freedom considered. Here, we collect many particles making up a composite body to a single centre of mass and orientation. Defining the CG interaction potential between the bodies that minimizes loss of information is non-trivial with no clear analytical solution. We use neural ordinary differential equations (ODE) to learn such CG potentials in a data-driven manner. We show a proof-of-concept application on a toy problem and outline the next steps towards an automated CG software pipeline.