Long-term excitation energy transfer predicted by a modified convolutional neural networks in the FMO complexes

• URL: http://arxiv.org/abs/2503.17430v3
• Date: Thu, 24 Apr 2025 04:26:48 GMT
• Title: Long-term excitation energy transfer predicted by a modified convolutional neural networks in the FMO complexes
• Authors: Yi-Meng Huang, Zi-Ran Zhao, Shun-Cai Zhao,
• Abstract summary: We propose an efficient CNNs scheme incorporating novel redundant time-functions to predict 100 picosecond (ps) excitation energy transfer (EET) in Fenna-Matthews-Olson complexes.<n>This method simplifies optimization and enhances learning efficiency, and demonstrates the accuracy, robustness, and efficiency of our approach in predicting quantum dissipative dynamics.
• Score: 0.49157446832511503
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In machine learning (ML), the risk of recursive strategies overfitting historical data has driven the development of convolutional neural networks (CNNs) in simulating quantum dissipative dynamics. In this work, we propose an efficient CNNs scheme incorporating novel redundant time-functions to predict 100 picosecond (ps) excitation energy transfer (EET) in Fenna-Matthews-Olson (FMO) complexes, in which the original time $t$ is normalized by mapping it to the [0, 1] range, allowing different functions focus on distinct time intervals, thereby effectively capturing the multi-timescale characteristics of EET dynamics. This method simplifies optimization and enhances learning efficiency, and demonstrate the accuracy, robustness, and efficiency of our approach in predicting quantum dissipative dynamics.

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