通过深度学习潜力的高碳化物融模拟
Viktor S Baidyshev1, Christian Tantardini2,3,4, Alexander G Kvashnin5
1Project Center for Energy Transition and ESG, Skolkovo Institute of Science and Technology, Bolshoi Blv. 30, Building 1, Moscow, 121205, Russian Federation.
Scientific reports
|November 20, 2024
概括
我们使用深度神经网络研究高碳化物 (HECN). 增加含量提高了融温度,度达到25%,从而提高了热性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 物理化学 物理化学
背景情况:
- 化温度是影响工业应用的关键材料属性.
- 高碳化物 (HECN) 是具有可调节性质的先进材料.
- 了解HECN的原子化行为对于材料设计至关重要.
研究的目的:
- 研究含量对 (TiZrTaHfNb) CxN1-x碳化物 (HECN) 化温度的影响.
- 使用原子模拟分析HECN从固态到液态的结构变化.
- 为了阐明融化温度随着含量增加而非线性增强的起源.
主要方法:
- 利用深度神经网络的潜力进行HECN的原子模拟.
- 预测的加热和冷却温度,以确定点.
- 分析了对相关性,并计算了液相中的.
主要成果:
- 化温度随着HECN中含量增加而增加.
- 观察到 (TiZrTaHfNb) C0.75N0.25 (25%) 的最大化温度为3580 ± 30K.
- 融化温度的非线性增强是由结构和变化解释的.
结论:
- 添加气显著改善了高化合物的化行为.
- 优化含量的HECN为开发先进的热材料提供了途径.
- 深度神经网络潜力为HECN属性提供了准确的原子学见解.
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