使用JAX-ReaxFF和神经网络潜力精确模拟MoS2属性的多重忠诚度方法
Kehan Wang1,2, Longkun Xu3, Wei Shao3
1State Key Laboratory of Tribology in Advanced Equipment (SKLT), Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
The journal of physical chemistry letters
|January 4, 2024
概括
我们开发了一种结合JAX-ReaxFF和神经网络潜力的新方法,以准确模拟像MoS2.2这样的材料. 这种方法减少了错误,并加速了各种材料系统的计算研究.
科学领域:
- 计算材料科学 计算材料科学
- 化学物理 化学物理
- 纳米技术 纳米技术
背景情况:
- 反应力场 (ReaxFF) 对于原子级化学反应建模至关重要.
- 由于分析公式,JAX-ReaxFF提供了高效的参数化,但可能会产生不准确的预测.
- 神经网络潜能 (NNP) 提供了准确性,但需要广泛的训练数据.
研究的目的:
- 引入一种新的多重真实性方法,将JAX-ReaxFF和NNP集成在一起.
- 为了提高材料模拟的准确性和效率.
- 应用和验证用于柔性电子的二硫化物 (MoS2) 方法.
主要方法:
- 开发了一种混合方法,将JAX-ReaxFF和NNP结合起来.
- 利用ReaxFF与隐含的物理信息,以节省成本,有效地生成预训练数据.
- 将该方法应用于模拟和验证的Mo-S-H系统.
主要成果:
- 多重真实性方法成功模拟了MoS2,一个2D半导体.
- 使用ReaxFF进行预训练数据生成,显著提高了NNP的准确性.
- 在Mo-S-H系统中实现了20%的平均平方根能量误差减少.
结论:
- 联合JAX-ReaxFF和NNP方法为准确的材料模拟提供了强大的策略.
- 这种方法通过减少数据要求和提高预测准确性来加速计算研究.
- 该方法可扩展到各种材料系统,推进材料发现.
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