在铁的bcc-hcp相位过渡中,用于机器学习的力场,使用随机表面行走进行战略采样
Fang Wang1, Zhi Yang1, Fenglian Li2
1College of Physics, Taiyuan University of Technology Jinzhong 030600 China xulichun@tyut.edu.cn.
RSC advances
|November 1, 2023
概括
研究人员开发了一个机器学习力场来模拟铁.
科学领域:
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
- 在材料中的机器学习
背景情况:
- 模拟铁等材料的相变对于理解它们在极端条件下的行为至关重要.
- 准确的力场对于可靠的分子动力学模拟是必不可少的.
研究的目的:
- 开发一种高效准确的基于机器学习的力场,用于模拟铁的体中心立方 (bcc) 到六角密集 (hcp) 阶段过渡.
- 通过高精度的计算和实验数据验证机器学习力场.
主要方法:
- 使用贝叶斯推理构建机器学习潜力.
- 分子动力学 (MD) 和随机地表行走 (SSW) 采样方法.
- 结构表示的SOAP (原子位置的平滑重叠) 描述符.
- 密度函数理论 (DFT) 计算用于验证.
主要成果:
- 开发的机器学习力场准确地复制了DFT计算的铁的能量,力和应力.
- 它显示了相位过渡空间的良好覆盖.
- 精确预测晶体结构参数,弹性常数和bcc和hcp铁相的散量模量.
结论:
- 机器学习力场为研究铁相过渡提供了有效和准确的工具.
- 这项工作为材料科学和固态物理研究提供了新的计算方法.
- 开发的潜力使我们能够更深入地了解铁在不同条件下的复杂行为.
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