硫化的机器学习原子间潜力:从热力学特性到晶体生长动力学
N M Chtchelkatchev1,2, R E Ryltsev3, V E Ankudinov4
1Vereshchagin Institute of High Pressure Physics, Russian Academy of Sciences, 108840 Moscow, Russia.
The Journal of chemical physics
|December 1, 2025
概括
一个新的机器学习潜力,DeePMD,准确地预测硫化的特性和晶体的生长. 它比经典潜能更准确,特别是在较低的温度下,可以进行更好的多尺度模拟.
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:
- 准确的原子间电位对于模拟材料特性和过程至关重要.
- 经典潜能往往难以捕捉半导体中的复杂行为,例如硫化 (BaS).
- 机器学习潜力为高保真模拟提供了一个有希望的替代方案.
研究的目的:
- 开发和验证一个基于神经网络的原子间潜力 (DeePMD) 对于BaS.
- 评估DeePMD的性能与热力学和生长性质的经典潜力相比.
- 评估DeePMD在晶体生长的多尺度模拟中的实用性.
主要方法:
- 使用第一原则模拟来训练DeePMD对固体和液体BaS的潜力.
- 用分子动力学模拟来计算体积特性和界面能量.
- 进行了晶体生长模拟和与动力相场模型的集成.
主要成果:
- 与经典的Rino潜力相比,DeePMD证明了对BaS密度和液体结构的更好的预测.
- 这两种潜能都重现了融化温度和接近融化的线性增长.
- DeePMD预测在较低温度 (<1800 K) 增强晶体生长速度,这表明自发核形成的可能性.
结论:
- 像DeePMD这样的机器学习潜力是精确的多尺度模拟晶体生长的宝贵工具.
- DeePMD提供了比经典潜能更具预测性的BaS特性和生长动力学模型.
- 这项工作突出了人工智能驱动的方法在材料科学研究的潜力.
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