Machine learned force and torque predictions for molecular dynamics of non-spherical colloids

Composite-rigid bodies of smaller spherical beads are commonly used to simulate non-spherical colloids with Molecular Dynamics (MD). To accurately represent their shape and to obtain the desired effective pair interactions between two rigid bodies, each body may need to contain hundreds of beads. Traditional MD calculate all the inter-body distances between the beads of the rigid bodies to find the net force and torque on them. These distance calculations are computationally costly and limit the number of rigid bodies that can be simulated. However, the effective interaction between the two rigid bodies depends only on the distance between their center of mass and their relative orientation. This implies the existence of a function capable of mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies, which would completely bypass the individual inter-body bead distance calculations. Deriving such a function analytically for nearly any non-spherical rigid body is a significant challenge. In this study, we have trained neural nets that take the pair configuration as input and give the forces and torques between the two rigid bodies (cylinders and cubes) as output. We show that MD simulations performed with neural net predicted forces and torques can accurately reproduce the structure and kinetics of the traditional simulations with explicit distance calculations. Neural-net assisted simulations can offer speed-ups, depending on system size and hardware.