开发一种机器学习的原子间潜力,用于探索中相变的压力依赖动力学
A Fantasia1, F Rovaris1, O Abou El Kheir1
1Department of Materials Science, University of Milano-Bicocca, 20125 Milano, Italy.
The Journal of chemical physics
|July 2, 2024
概括
我们开发了一种新的计算潜力,用于研究中压力诱导的相变,准确预测动力障碍,并模拟大规模转换. 这个工具增强了对的理解.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 固态化学 固态化学
背景情况:
- 对像这样的材料的压力依赖相变的研究对于了解它们在极端条件下的行为至关重要.
- 精确预测动力障碍对于建模相变换路径至关重要.
- 对于大规模的模拟,传统的方法在计算上可能很昂贵.
研究的目的:
- 引入一种新的数据驱动潜力,用于模拟散装中的压力诱导相位过渡.
- 准确估计与这些转换相关的动力障碍.
- 为了实现相变的大规模模拟,例如R8到β-Sn过渡.
主要方法:
- 通过推推弹性带方法的最小能量路径配置构建了一个数据库.
- 通过密度函数理论 (DFT) 计算的能量,力和应力训练了一种机器学习潜力.
- 训练的潜力根据DFT计算进行了验证,并用于异热-异热模拟.
主要成果:
- 数据驱动的潜力显示出与DFT计算在广泛的压力范围内有着显著的一致性.
- 潜力准确地预测了相变的动力障碍.
- 大规模模拟显示,在R8到β-Sn相转换过程中发生了局部核形成.
结论:
- 开发的潜力提供了一个计算效率高,准确的方法来研究压力诱导的相变.
- 这种方法有助于研究复杂的转换机制和运动路径.
- 这些发现为德国在不同压力下的行为提供了宝贵的见解.
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