First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation
First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation
复制标题
第一性原理和机器学习预测严重晶格畸变高熵合金的弹性与实验验证
DOI：
10.1016/j.actamat.2019.09.026
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发表时间：
2019-12-01
期刊：
影响因子：
9.4
通讯作者：
Chen, Wei
中科院分区：
文献类型：
--
作者：
Kim, George;Diao, Haoyan;Chen, Wei
正如在许多纳米结构中观察到的那样，刚度通常随着晶格畸变引起的应变而增加。由于组成元素的尺寸差异，高熵合金中自然存在严重的晶格畸变。单相面心立方（FCC） Al0.3CoCrFeNi HEA，其组成元素之间存在较大的尺寸差异，是采用原位中子衍射（ND）表征和第一性原理计算相结合的实验和计算方法研究弹性性能与晶格畸变关系的理想体系。从优化的特殊准随机结构（SQS）计算中分析原子间距离分布，发现HEA具有高度的晶格畸变。当明确考虑晶格畸变时，用SQS计算的弹性性能与HEA的实验测量结果非常吻合。计算的弹性常数值与ND测量值的误差在5%以内。优化后的SQS与理想点阵位置的SQS计算结果的比较表明，点阵畸变导致刚度降低。优化后的SQS的体积模量为177 GPa，而理想晶格SQS的体积模量为194 GPa。还实现了机器学习（ML）建模，以探索使用快速，计算效率高的模型来预测HEAs的弹性模量。在大型无机结构数据集上训练的ML模型可以准确预测HEA的弹性特性。ML模型还证明了体模量和剪切模量对几种材料特征的依赖性，这些特征可以作为调整HEAs弹性特性的指南。(C) 2019材料学报Elsevier Ltd.出版。版权所有。
Stiffness usually increases with the lattice-distortion-induced strain, as observed in many nanostructures. Partly due to the size differences in the component elements, severe lattice distortion naturally exists in high entropy alloys (HEAs). The single-phase face-centered-cubic (FCC) Al0.3CoCrFeNi HEA, which has large size differences among its constituent elements, is an ideal system to study the relationship between the elastic properties and lattice distortion using a combined experimental and computational approach based on in-situ neutron-diffraction (ND) characterizations, and first-principles calculations. Analysis of the interatomic distance distributions from calculations of optimized special quasi random structure (SQS) found that the HEA has a high degree of lattice distortion. When the lattice distortion is explicitly considered, elastic properties calculated using SQS are in excellent agreement with experimental measurements for the HEA. The calculated elastic constant values are within 5% of the ND measurements. A comparison of calculations from the optimized SQS and the SQS with ideal lattice sites indicate that the lattice distortion results in the reduced stiffness. The optimized SQS has a bulk modulus of 177 GPa compared to the ideal lattice SQS with a bulk modulus of 194 GPa. Machine learning (ML) modeling is also implemented to explore the use of fast, and computationally efficient models for predicting the elastic moduli of HEAs. ML models trained on a large dataset of inorganic structures are shown to make accurate predictions of elastic properties for the HEA. The ML models also demonstrate the dependence of bulk and shear moduli on several material features which can act as guides for tuning elastic properties in HEAs. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.