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机器学习辅助的高通量第一原理计算,预测μ阶段的形成能量

Yue Su¹, Jiong Wang¹, You Zou²

¹State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China.

|October 16, 2023

概括

机器学习准确地预测了高温合金中μ相的形成能量,将计算成本降低了50%以上. 这种方法增强了材料设计,并通过图形用户界面提供可访问的数据.

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
合金设计设计合金设计

背景情况:

微相是高温合金中的关键,但脆弱的组件.
准确的形成能量数据对于设计先进的高温合金至关重要.
传统的第一原则计算是计算密集且耗时的.

研究的目的:

开发一种高效准确的机器学习 (ML) 模型,用于预测μ阶段的形成能量.
为了减少与计算μ阶段的形成能量相关的计算负担.
创建一个用户友好的工具来访问μ相位形成能量数据.

主要方法:

使用了六个机器学习算法和两种评估方法.
在使用 10 倍交叉验证的 1036 个二进制配置数据集上训练模型.
采用多层感知子 (MLP) 算法以获得最佳性能.

主要成果:

实现了二进制配置的平均绝对误差 (MAE) 23.906 meV/原子,三进制配置的 32.754 meV/原子.
与传统方法相比,至少减少了52%的计算时间.
预测的格子参数 (a 和 c) 具有较低的错误率 (分别为 0.479% 和 0.578%).

结论:

开发的ML模型准确地预测了μ阶段形成能量和格子参数.
这种方法显著加速了高温合金的材料设计过程.
创建了一个图形用户界面 (GUI),以改善研究人员的数据可访问性.

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