层状酸盐K0.8Li0.27Ti1.73O4的导热率通过深度学习探讨了原子间潜力的热导率
Yan Gao1, Xinshuo Wang1, Huiyu Yuan1
1Henan Key Laboratory of High Temperature Functional Ceramics, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450052, China.
The Journal of chemical physics
|March 24, 2025
概括
机器学习通过开发深度学习潜力 (DLP) 准确地预测分层氧化物中的导热率. 这种新方法克服了复杂材料的传统方法的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 化学工程是化学工程的重要组成部分.
背景情况:
- 难以预测复杂,低对称的层状氧化物中的导热率.
- 传统的方法,如博尔兹曼运输方程和分子动力学,在准确性和范围上都有局限性.
研究的目的:
- 开发一种机器学习策略,准确预测分层氧化物的导热率.
- 使用深度神经网络为K0.8Li0.27Ti1.73O4 (KLTO) 创建可靠的原子间潜力.
主要方法:
- 利用深度神经网络模型为KLTO产生深度学习潜力 (DLP).
- 验证了DLP与密度函数理论对原子力,能量和弹性属性的验证.
- 使用开发的DLP计算出平面外导热率.
主要成果:
- DLP显示出与密度函数理论的良好一致.
- 计算的导热率 (0.37 W m−1 K−1) 与实验值 (0.28 W m−1 K−1) 非常接近.
结论:
- 机器学习框架为预测分层材料中的原子间潜力提供了一个有前途的方法.
- 这种方法增强了复杂的氧化物系统中导热性的理论研究.
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