Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910643735494656 |
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| author | Subramanian, Akshay Qu, Shuhui Park, Cheol Woo Liu, Sulin Lee, Janghwan Gómez-Bombarelli, Rafael |
| author_facet | Subramanian, Akshay Qu, Shuhui Park, Cheol Woo Liu, Sulin Lee, Janghwan Gómez-Bombarelli, Rafael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching Subramanian, Akshay Qu, Shuhui Park, Cheol Woo Liu, Sulin Lee, Janghwan Gómez-Bombarelli, Rafael Materials Science Artificial Intelligence 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. |
| title | Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2410.07539 |