使用高维神经网络潜力探索氧化纳米集群的稳定结构
Huabing Cai1,2, Qinghua Ren1, Yi Gao2,3,4
1Department of Chemistry, Shanghai University 99 Shangda Road Shanghai 200444 China qinghua.ren@shu.edu.cn.
Nanoscale advances
|May 16, 2024
概括
研究人员使用神经网络和DFT开发了一种新方法,用于发现氧化集群的稳定,金字塔状结构. 这些超小集群表现出高活性和氧气释放,对于工业应用至关重要.
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
- 纳米技术纳米技术
背景情况:
- 氧化集群是重要的工业催化剂,由于氧气储存和氧化还原特性.
- 由于复杂的结构和缺乏高效的采样方法,对这些集群的原子精确结构确定具有挑战性.
研究的目的:
- 开发一种可靠的计算方法来识别氧化集群的全球最小能量结构.
- 为了研究特定氧化集群化合物的结构稳定性和电子特性 (Ce14O28,Ce14O27,Ce14O29).
主要方法:
- 结合神经网络算法与密度函数理论 (DFT) 计算,创建一个高维潜力.
- 使用神经网络潜力增强的分子动力学,用于在高温下进行结构稳定性分析.
- 探索了超过10万种配置以识别低能结构.
主要成果:
- 确定了类似金字塔的结构作为Ce14O28,Ce14O27和Ce14O29星团的最低能量配置 (约. 1.0 nm) 的时间.
- 在高温下证明了这些金字塔状集群的结构稳定性.
- 电子结构分析显示了高的催化活性和容易释放氧气.
结论:
- 神经网络潜能为探索金属氧化物纳米集群的稳定结构提供了一种有效的方法.
- 这些发现为用于催化应用的氧化集群的结构属性关系提供了洞察力.
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