使用机器学习的原子间潜力来研究素矿的相位过渡,机械行为和晶格导热率
Yongbo Shi1, Yuanyuan Chen1, Haikuan Dong1
1College of Physical Science and Technology, Bohai University, Jinzhou 121013, P. R. China. donghaikuan@163.com.
Physical chemistry chemical physics : PCCP
|November 7, 2023
概括
机器学习准确地预测了素矿的特性. 模拟显示了相位过渡和低导热率,证实了机器学习潜力的可靠性,用于材料科学研究.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 密度函数理论 (DFT) 的计算对于大型系统来说在计算上是昂贵的.
- 机器学习 (ML) 提供了一种方法,可以从DFT数据中开发精确的原子间潜力.
- 素矿是具有复杂相位行为的有前途的材料.
研究的目的:
- 调查氧矿的相位转换,机械性能和导热性.
- 利用基于机器学习的潜力进行大规模分子动力学 (MD) 模拟.
- 为了克服DFT在捕捉尺寸和温度效应方面的局限性.
主要方法:
- 采用机器学习方法提取的原子间潜力与DFT数据相匹配.
- 通过使用神经网络潜力 (NNP) 对16000个原子进行了大规模分子动力学 (MD) 模拟.
- 分析了相位过渡,机械参数 (波松比,拉力强度,散装模量) 和晶格导热率.
主要成果:
- 在加热时观察到一个清晰的相变序列 (正方形 → 四边形 →立方形).
- 在CsPbCl3和CsPbBr3中发现可逆相过渡,但在冷却后在CsPbI3中只有部分可逆性.
- 预测这些材料在室温下极低的导热率.
- 成功预测了诸如波桑比率,抗拉强度和散装模量等机械性质.
结论:
- 机器学习潜能准确地重现DFT数据,并使大规模的MD模拟成为可能.
- 这项研究提供了有关氧矿相变动态和机械行为的见解.
- 这些发现验证了使用ML潜力来研究材料特性,克服了DFT的局限性.
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