一个积极的学习力场,用于有机金属复杂晶体的热传输特性
Wenjie Zhang1, Weitang Li2, Zhigang Shuai1,2
1MOE Key Laboratory of Organic OptoElectronics and Molecular Engineering, Department of Chemistry, Tsinghua University, Beijing 100084, China. zgshuai@tsinghua.edu.cn.
机器学习力场准确地预测有机金属热电材料的热导率. 这一进步有助于开发用于高效能源转换技术的新材料.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 能源转换 能源转换
背景情况:
- 对有机金属热电材料来说,准确预测格子导热率至关重要.
- 为复杂的有机金属材料,特别是用于热传输,开发精确的力场是一项挑战.
- 机器学习力场看起来很有前途,但在大型复杂物和分子间/分子内相互作用方面遇到困难.
研究的目的:
- 为有机金属材料开发一种高度预测的机器学习力场.
- 解决模拟大型有机金属复合物及其相互作用的挑战.
- 使用先进的计算方法,准确计算铜酸的导热率.
主要方法:
- 采用积极学习方法与深度神经网络相结合.
- 使用局部环境描述符进行材料表示.
- 使用格林-库博法计算的导热率.
主要成果:
- 开发了一种能够捕捉高阶多体相互作用的机器学习力场.
- 在300K时,铜酸的计算导热率为0.49W m-1K-1.1.
- 取得的结果与实验发现 (0.39 W m-1 K-1) 一致,优于经典力场.
结论:
- 机器学习的力场可以有效地描述金属有机复杂系统中的相互作用.
- 这种方法显著推进了有机金属热电材料的开发和发现.
- 开发的力场为这些材料中更准确的热传输预测提供了途径.
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