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arXiv 2405.12232physics.chem-phquant-ph
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基于长短期记忆网络预测光系统II反应中心的量子动力学演化

Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center

Zi-Ran Zhao, Shun-Cai Zhao, Yi-Meng Huang

AI总结:

研究提出结合误差阈值训练的LSTM网络,利用8 fs内的模拟数据预测光系统II反应中心的长时电荷传输行为,预测差异达$10^{-4}$量级,,为分子尺度光合作用研究提供了新范式。

摘要:

从有限的理论模拟数据预测未来的物理行为,是由人工智能与量子物理融合驱动的新兴研究范式。本研究在光系统II反应中心(PSII-RC)中,使用深度学习模型——结合误差阈值训练方法的长短期记忆(LSTM)网络,预测了较长时间尺度上的电荷传输(CT)行为。研究利用8 fs以内的理论模拟数据训练改进的LSTM网络,在相较于训练集采集时间更长的周期内,产生了与训练集相比差异在$10^{-4}$量级上的独特预测。结果突显了LSTM在揭示超越传统量子物理方法的CT底层物理机制方面的潜力。这些发现值得进一步研究,以充分探索LSTM在推进分子尺度光合作用理解方面的范围和功效。

英文摘要:

Predicting future physical behavior from limited theoretical simulation data is an emerging research paradigm driven by the integration of artificial intelligence and quantum physics. In this work, charge transport (CT) behavior was predicted over extended time scales using a deep learning model-the long short-term memory (LSTM) network with an error-threshold training method-in the photosystem II reaction center (PSII-RC). Theoretical simulation data within 8 fs were used to train the modified LSTM network, yielding distinct predictions with differences on the order of $10^{-4}$ over prolonged periods compared to the training set collection time. The results highlight the potential of LSTM to uncover the underlying physics governing CT beyond conventional quantum physical methods. These findings warrant further investigation to fully explore the scope and efficacy of LSTM in advancing our understanding of photosynthesis at the molecular scale.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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