来自多体神经网络的电极化替代品
Xiang Li1, Yubing Qian1,2, Ji Chen2,3
1ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People's Republic of China.
Physical review letters
|May 10, 2024
概括
这项研究引入了一种新的神经网络量子蒙特卡洛方法,用于准确的介电响应计算. 该方法有效地捕获电子相关性,优于传统方法,并与实验数据保持一致.
科学领域:
- 计算物理 计算物理
- 量子化学 是一个量子化学.
- 材料科学 材料科学 材料科学
背景情况:
- 准确的介电响应计算对于理解材料特性至关重要.
- 传统方法往往近似电子相关性,限制准确性.
- 开发高精度的电子结构方法仍然是一个重大挑战.
研究的目的:
- 开发和验证用于初始介电响应计算的新计算方法.
- 为了准确地将电子相关性纳入极化计算.
- 评估新方法在各种材料系统中的性能.
主要方法:
- 采用神经网络波函数代替.
- 结合量子蒙特卡洛方法进行相关性处理.
- 应用于各种系统:原子,1D链条,2D板块,3D立方体.
主要成果:
- 与传统的密度函数理论相比,取得了更好的结果.
- 证明与高度准确的计算和实验数据的一致性.
- 成功地恢复了双层石墨烯介电常数的厚度依赖.
结论:
- 神经网络量子蒙特卡洛方法为准确的极化计算提供了一个强大的工具.
- 这种方法显著改善了材料中电子相关性的描述.
- 为凝聚物质物理和化学中更可靠的预测提供了一条途径.
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