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arXiv 1907.08473physics.plasm-phcond-mat.str-el
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热稠密电子气体的静态局域场修正:从头算路径积分蒙特卡罗研究和机器学习表示

The Static Local Field Correction of the Warm Dense Electron Gas: An ab Initio Path Integral Monte Carlo Study and Machine Learning Representation

Tobias Dornheim, Jan Vorberger, Simon Groth, Nico Hoffmann, Zhandos Moldabekov, Michael Bonitz

摘要:

对天体物理物体中出现的极端密度和温度下的物质进行研究,以及利用高强度激光进行最先进的实验,对许多应用都具有很高的现实意义。虽然没有总体的理论,这种制度的存在,准确的数据的相关电子的密度响应外部扰动是至关重要的。在这种情况下,关键的数量是由本地场校正(LFC),它提供了一个波矢量解析描述的交换相关效应。在这项工作中,我们提出了广泛的新路径积分蒙特卡罗(PIMC)结果的静态LFC的均匀电子气,随后用于训练一个完全连接的深度神经网络。这使我们能够提出一个连续的表示LFC相对于波矢量,密度和温度覆盖整个温暖的致密物质制度。PIMC的数据和神经网络结果都可以在线获得。此外,我们预计从头计算与机器学习方法的组合是一个很有前途的策略,许多应用。

英文摘要:

The study of matter at extreme densities and temperatures as they occur in astrophysical objects and state-of-the art experiments with high-intensity lasers is of high current interest for many applications. While no overarching theory for this regime exists, accurate data for the density response of correlated electrons to an external perturbation are of paramount importance. In this context, the key quantity is given by the local field correction (LFC), which provides a wave-vector resolved description of exchange-correlation effects. In this work, we present extensive new path integral Monte Carlo (PIMC) results for the static LFC of the uniform electron gas, which are subsequently used to train a fully connected deep neural network. This allows us to present a continuous representation of the LFC with respect to wave-vector, density, and temperature covering the entire warm dense matter regime. Both the PIMC data and neural-net results are available online. Moreover, we expect the presented combination of ab initio calculations with machine-learning methods to be a promising strategy for many applications.

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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