arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2405.04876cond-mat.mtrl-sci
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结合ab initio计算与机器学习加速层错能预测

Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

Albert Linda, Md. Faiz Akhtar, Shaswat Pathak, Somnath Bhowmick

AI总结:

研究结合ab initio计算与基于Friedel模型的机器学习算法,将广义层错能预测速度提升约80倍,预测结果与实验数据一致,为基于层错能的快速材料筛选与合金设计提供了高效方法。

摘要:

层错能(SFEs)是理解金属与合金变形机制的重要参数,从ab initio计算中获取SFEs的先验知识对合金设计至关重要。本研究采用的机器学习(ML)算法在预测广义层错能(GSFE)时实现了约80倍的加速,而GSFE若直接通过密度泛函理论(DFT)计算获取通常计算成本高昂,对于合金尤其如此。用于训练ML算法的特征源自基于物理的Friedel模型,揭示了d电子物理与过渡金属及合金变形行为之间的联系。基于ML模型的预测与实验数据一致。该模型通过提供基于层错能的快速材料筛选方法,有助于加速合金设计。

英文摘要:

Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations being crucial for alloy design. Machine learning (ML) algorithms employed in the present work demonstrate approximately 80 times acceleration in predicting generalized stacking fault energy (GSFE), which is otherwise computationally expensive to obtain directly from density functional theory (DFT) calculations, particularly for alloys. The features used to train the ML algorithms stem from the physics-based Friedel model, revealing a connection between the physics of d-electrons and the deformation behavior of transition metals and alloys. Predictions based on the ML model are consistent with experimental data. This model could aid in accelerating alloy design by offering a rapid method for screening materials based on stacking fault energies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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