深度学习的e-ZrX_{2}系列的原子间潜力 (X=H,D,T)
Kunyang Cheng1,2, Xiujuan Cheng2, Mingyang Shi2
1Sichuan University, College of Physics, Chengdu 610064, China.
Physical review. E
|June 19, 2025
概括
-二 (ɛ-ZrH2) 的深度学习潜力可以准确预测材料的性质. 这些潜力改善了用于核能和储存应用的分子动态模拟.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 核工程 核工程是指核工程.
背景情况:
- -二化物 (ɛ-ZrH2) 对于核能,储存和催化非常重要.
- 精确的材料性能模拟对于推进这些应用程序至关重要.
研究的目的:
- 为e-ZrH2.2开发精确的深度学习原子间潜力 (DP).
- 增强分子动力学 (MD) 对基化物的模拟.
主要方法:
- 使用密度函数理论 (DFT) 数据对 ɛ-ZrH2.2 的训练深度学习潜力.
- 纳入了用于照射模拟的H同位素效应.
- 将DP性能与修改嵌入原子方法 (MEAM) 潜力进行比较.
主要成果:
- 开发的DP准确地预测了e-ZrH2的结构,机械和热力学特性.
- 与传统的MEAM潜力相比,DP显示出更高的性能.
- 在辐射下DP方便对e-ZrX2 (X=H,D,T) 的MD模拟.
结论:
- 开发的DP为模拟基化物提供了重大进步.
- 这些潜力使得用于储存和核应用的MD模拟更加可靠.
- 纳入同位素效应扩大了MD模拟对被辐射的材料的适用性.
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