使用机器学习方案探索氧化碳化的热力学稳定性
Ruizhi Qiu1, Jun Tang1, Jinfan Chen1
1Science and Technology on Surface Physics and Chemistry Laboratory, Mianyang 621908, Sichuan, China. qiuruizhi@caep.cn.
Physical chemistry chemical physics : PCCP
|February 16, 2024
概括
机器学习准确地预测氧化碳的稳定性,有助于燃料制造和腐蚀研究. 这种计算方法可以高精度地识别稳定的结构.
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
- 核工程 核工程是指核工程.
背景情况:
- 氧化碳对制造核燃料和了解腐蚀至关重要.
- 预测PuOC1-的热力学稳定性对于材料设计至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测氧化碳化物 (PuOC1-) 的热力学稳定性.
- 为了比较不同ML方案和结构描述器的性能,用于此应用程序.
主要方法:
- 用Hubbard校正的密度函数理论 (DFT) 来生成训练数据.
- 四个机器学习模型被训练并使用三个结构描述符进行评估.
- 通过比较预测和DFT计算的混合能量和格子参数来评估最佳ML模型的准确性.
主要成果:
- 最优的ML模型实现了高精度,混合能量的平均误差为3meV/原子,格子参数的平均误差为0.003 Å.
- ML模型成功预测了凸的船体,并为PuOC1-.确定了几个稳定的有序原子结构.
- 在有序结构中增强的稳定性与Pu 5f/6d和C/O 2p轨道之间的强烈杂交有关,形成强大的Pu-C和Pu-O键.
结论:
- 机器学习提供了一个高度准确和高效的方法来预测氧化碳化的热力学稳定性.
- 这些发现提供了关于稳定的氧化碳化相的形成及其结合特性的见解.
- 这种方法可以加速先进核材料的发现和设计.
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