Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
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DOI：
10.1016/j.actamat.2022.117924
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发表时间：
2022-04
期刊：
影响因子：
9.4
通讯作者：
G. Vazquez;P. Singh;D. Sauceda;Richard Couperthwaite;Nicholas Britt;Khaled Youssef;Duane D. Johnson-Duane-D.
中科院分区：
文献类型：
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作者：
G. Vazquez;P. Singh;D. Sauceda;Richard Couperthwaite;Nicholas Britt;Khaled Youssef;Duane D. Johnson-Duane-D.
我们将基于磁流变仪的刚度矩阵和弹性模量分析模型与平均场方法相结合，以加速评估高熵合金的技术有用特性，如强度和延展性。弹性性质的模型训练使用确定独立筛选（SIS）和稀疏化算子（SO）方法，产生最佳分析模型，该模型由有意义的原子特征构建，以预测目标性质。计算上便宜的分析描述符进行训练，使用数据库的弹性性能确定的密度泛函理论的二元和三元子集的Nb-Mo-Ta-W-V耐火合金。从指数级大的特征空间中提取的最佳Elastic-SISSO模型对目标属性进行了非常准确的预测，与其他模型相似或更好，其中一些已从现有实验中得到验证。我们还表明，电负性方差和弹性模量可以直接预测耐火HEAs的延展性和屈服强度的趋势，并揭示了有前途的合金浓度区域。
We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. We also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.