Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching

TL;DR

This paper introduces a dual-scale flow matching approach to efficiently generate molecular clusters, improving sampling accuracy and computational speed for amorphous molecular solids relevant to organic electronics.

Contribution

The authors develop a novel dual-scale flow matching method that separates coarse-grained and all-atom stages, enhancing efficiency and accuracy over existing single-scale models.

Findings

Outperforms single-scale flow matching in accuracy

Reduces computational cost of molecular cluster generation

Effective on Y6 molecular cluster dataset

Abstract

Amorphous molecular solids offer a promising alternative to inorganic semiconductors, owing to their mechanical flexibility and solution processability. The packing structure of these materials plays a crucial role in determining their electronic and transport properties, which are key to enhancing the efficiency of devices like organic solar cells (OSCs). However, obtaining these optoelectronic properties computationally requires molecular dynamics (MD) simulations to generate a conformational ensemble, a process that can be computationally expensive due to the large system sizes involved. Recent advances have focused on using generative models, particularly flow-based models as Boltzmann generators, to improve the efficiency of MD sampling. In this work, we developed a dual-scale flow matching method that separates training and inference into coarse-grained and all-atom stages and enhances both the accuracy and efficiency of standard flow matching samplers. We demonstrate the effectiveness of this method on a dataset of Y6 molecular clusters obtained through MD simulations, and we benchmark its efficiency and accuracy against single-scale flow matching methods.