用双尺度生成模型高效构建分子团簇，加速有机太阳能电池材料模拟。
Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching
- 分粗粒度与全原子两阶段训练，提升采样效率
- 在Y6分子团簇数据集上生成精度显著优于单尺度方法
- 适合需快速生成分子构型的材料研发人员
无定形分子固体因其机械柔性与溶液可加工性，是无机半导体的有前景替代品。其堆积结构对电子与输运性能至关重要，直接影响有机太阳能电池（OSCs）的效率。然而，计算这些光电性能需通过分子动力学（MD）模拟生成构象集合，因系统规模大而计算成本高。近期研究采用生成模型（特别是基于流的模型作为玻尔兹曼生成器）提升MD采样效率。本文提出一种双尺度流匹配方法，将训练与推理分离为粗粒度和全原子阶段，显著提升标准流匹配采样器的准确性和效率。我们在通过MD模拟获得的Y6分子团簇数据集上验证了该方法的有效性，并与单尺度流匹配方法进行了效率与精度对比。
原文摘要 · Abstract (English)
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.
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