加速电子传输和超导预测。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Nature computational science Pub Date : 2024-08-16 DOI:10.1038/s43588-024-00678-5
Ting Cao
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引用次数: 0

摘要

通过开发一种机器学习框架,最近的一项研究大大加快了电子-声子耦合的计算速度,使预测和理解一系列重要物理现象(包括复杂材料中的电子传输、热载流子弛豫和超导性)在计算上变得可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Accelerating predictions of electronic transport and superconductivity
By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.
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