通过机器学习驱动的原子模型,揭示超和无形中的化学状态
Simon Gramatte1,2,3,4, Olivier Politano2, Noel Jakse5
1Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, Feuerwerkerstrasse 39, Thun, CH-3602 Switzerland.
概括
准确的结构模型对于理解无形材料中的至关重要. 这项研究引入了一种快速模拟方法,将含量与化学变化联系起来,帮助开发基于的技术.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 技术是技术的一种技术.
背景情况:
- 在无形材料中描述的化学状态对于推进基于的技术至关重要.
- 在这些系统中对的实验探测具有挑战性,需要准确的结构模型来解释.
研究的目的:
- 引入一种快速的原子模拟技术,用于生成具有现实的含量的无形结构.
- 建立一种可靠的方法,将含量与无形材料中的实验可访问的化学变化联系起来.
主要方法:
- 利用基于ab initio的机器学习的原子间潜力进行原子模拟.
- 在原子层沉积温度下使用有缺陷的晶体氧化物结构的回火.
- 执行电荷密度和部分贝德电荷计算,以分析化学状态.
主要成果:
- 模拟技术准确地复制了无形的实验密度和结构.
- 较高的含量与偏好OH联结物相关.
- 较低的含量导致了不同的化学状态和结.
结论:
- 开发的模拟方法提供了含量和Al Auger参数化学变化之间的可靠联系.
- 该方法有助于设计和开发下一代相关材料.
- 精确的结构建模是理解无形系统中的行为的关键.
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