拉德尔斯登 - 波普尔石化物中的多元多态性.
Prakriti Kayastha1, Erik Fransson2, Paul Erhart2
1Northumbria University, School of Engineering, Physics, and Mathematics, Newcastle upon Tyne, NE1 8QH, United Kingdom.
我们使用机器学习来模拟Ruddlesden-Popper (RP) 素化物,发现新的结构和相位过渡. 这项工作解锁了这些先进的分层半导体调整特性的新策略.
科学领域:
- 材料科学 材料科学 材料科学
- 固态化学 固态化学
- 计算材料科学科学 计算材料科学
背景情况:
- 拉德尔斯登 - 波珀 (RP) 石化素是具有调节性质和稳定性的分层半导体.
- 虽然RP氧化物得到了很好的研究,但RP素的结构性行为尚不清楚.
- 利用RP素的结构多样性可以带来先进的功能.
研究的目的:
- 为了研究同类RP系列Ba_{n+1}Zr_{n}S_{3n+1}的结构行为.
- 开发一个预测模型,以了解相位过渡和结构性质.
- 在RP素化物中识别新的多态体及其特征.
主要方法:
- 开发一个高精度的机器学习的原子间潜力.
- 大规模的分子动力学模拟Ba_{n+1}Zr_{n}S_{3n+1}系列.
- 对模拟结果与实验数据的验证.
主要成果:
- 在Ba_{n+1}Zr_{n}S_{3n+1}序列中确定各种n值的新多态.
- 预测已识别的多态体的相位过渡温度.
- 在n=1阶段观察平面内负热膨胀.
- 在n=1和n=3阶段发现了不寻常的上升对称性破坏.
- 在n≥3.3的阶段观察了新的依赖层的倾斜模式.
- 观察到的行为与ZrS_{6}八面体旋转和BaS的相互作用的相关性.
结论:
- 这项研究揭示了RP素化物中前所未有的结构性行为.
- 这些发现表明了调整属性和实现高级功能的新途径.
- 机器学习潜力是有效的探索复杂的材料系统,如RP chalcogenides.
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