在机器学习分子动力学中,纠正力错误导致的格子导热率低估
Xiguang Wu1, Wenjiang Zhou2,3, Haikuan Dong4
1Guangzhou Key Laboratory of Low-Dimensional Materials and Energy Storage Devices, School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China.
The Journal of chemical physics
|July 1, 2024
概括
机器学习潜力 (MLP) 经常低估格子导热率 (LTC). 这项研究通过从引入的力噪声进行推断,纠正了MLP中的LTC低估,与实验数据达成了很好的一致.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 机器学习潜力 (MLP) 对于热传输的分子动力学模拟至关重要.
- 现有的文献表明,MLP通常低估固体中的晶格导热率 (LTC).
研究的目的:
- 量化分析MLP对LTC的低估,特别是神经进化潜力 (NEP).
- 开发和验证一项用于MLP准确预测LTC的校正方法.
主要方法:
- 作为低估LTC的原因,研究了MLP中的强度安装错误.
- 提出了通过Langevin恒温器引入受控力噪声并推断到零误差的纠正方法.
- 分析了光谱特性,以了解低估的起源.
主要成果:
- 在MLP中强制安装错误直接导致低估LTC,通过作为外部干扰.
- 拟议的校正方法成功地将模拟的LTC与,化,石墨烯和 telluride 的实验值相协调.
- 低估主要与低频谱中因力误差而增加的声子散射有关.
结论:
- 开发的校正方法有效地解决了MLP对LTC的系统低估问题.
- 使用MLP精确预测热传输特性,可以通过适当的错误纠正来实现.
- 了解力误差对音声动态的影响是改善材料模拟MLP的关键.
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