使用机器学习方法预测金属/氧化物接口的溶液分离
Yizhou Lu1, Blas Pedro Uberuaga2, Samrat Choudhury1
1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.
Molecules (Basel, Switzerland)
|August 28, 2025
概括
这项研究将密度函数理论 (DFT) 与机器学习 (ML) 结合起来,预测金属/氧化物界面上的溶液分离. 机器学习方法显著降低了计算成本,同时保持了预测分离行为的高准确性.
科学领域:
- 材料科学
- 计算材料科学
- 表面科学
背景情况:
- 金属/氧化物接口的原子结构和化学结构决定了材料的特性.
- 由于原子数量很大,使用 DFT 来研究具有不合适位移的半连贯接口是计算密集的.
研究的目的:
- 为了探索Fe/Y2O3接口的溶液分离行为,核反应堆覆盖的模型.
- 开发和验证机器学习 (ML) 方法与DFT计算相结合,用于预测溶液分离.
- 确定影响溶液分离的关键因素并降低计算成本.
主要方法:
- 使用密度函数理论 (DFT) 来计算分离能量 (例如.
- 使用DFT衍生ESeg数据开发和训练机器学习 (ML) 模型.
- 在金属/氧化物界面影响溶液分离的化学和几何因素.
主要成果:
- 确定了影响溶液分离的关键化学和几何因素及其对ESeg的竞争效应.
- 在使用ML模型预测特定Fe/Y2O3接口的溶液分离时取得了高准确性.
- 证明了ML模型在不同的Fe/Y2O3接口定向下预测分离的能力,与DFT相比,计算成本降低了45倍以上.
结论:
- 结合DFT-ML方法可以准确地预测金属/氧化物界面上的溶液分离.
- 机器学习模型显著降低了研究复杂接口的计算成本.
- 这种方法为了解和设计核等应用材料提供了强大的工具.
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