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arXiv 1609.02552cond-mat.str-el
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强关联费米子的机器学习相

Machine Learning Phases of Strongly Correlated Fermions

Kelvin Ch'ng, Juan Carrasquilla, Roger G. Melko, Ehsan Khatami

摘要:

机器学习为凝聚态物理中的相分类问题提供了前所未有的视角。我们采用神经网络机器学习技术来区分立方晶格上强相关费米子的有限温度相。我们表明,一个三维卷积网络训练辅助场的配置产生的量子蒙特卡罗模拟的哈伯德模型可以正确地预测磁相图的平均密度为1(半填充)的模型。然后,我们使用在半填充时训练的网络来探索当系统从半填充掺杂时转变温度的趋势。这种迁移学习方法预测,磁相的不稳定性延伸到该区域中的至少5%掺杂。我们的研究结果为相关量子多体系统中的其他机器学习应用铺平了道路。

英文摘要:

Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling). We then use the network, trained at half filling, to explore the trend in the transition temperature as the system is doped away from half filling. This transfer learning approach predicts that the instability to the magnetic phase extends to at least 5% doping in this region. Our results pave the way for other machine learning applications in correlated quantum many-body systems.

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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