TY - JOUR
T1 - A new method for an old topic
T2 - Efficient and reliable estimation of material bulk modulus
AU - Wang, Peng
AU - Qin, Yi
AU - Cheng, Ming
AU - Wang, Guanjie
AU - Xiu, Dongbin
AU - Sun, Zhimei
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/7
Y1 - 2019/7
N2 - Bulk modulus is a key material property which provides fundamentals on the chemical bonding nature of a material as well as for deriving other related material properties, such as Young's modulus and Grüneinsen constant. Traditional numerical methods of estimating bulk modulus often involve layers of approximations and cost considerably. In this work, we propose a novel and efficient numerical framework to estimate bulk modulus using the ab initio calculated data. Based on the generalized polynomial chaos expansion (gPC) method, our approach does not impose any physical priori and is mathematically rigorous and versatile. We have demonstrated the reliability and efficiency of the proposed method by estimating the bulk modulus of Ti3SiC2 and Sb2Te3 with comparison to conventional methods.
AB - Bulk modulus is a key material property which provides fundamentals on the chemical bonding nature of a material as well as for deriving other related material properties, such as Young's modulus and Grüneinsen constant. Traditional numerical methods of estimating bulk modulus often involve layers of approximations and cost considerably. In this work, we propose a novel and efficient numerical framework to estimate bulk modulus using the ab initio calculated data. Based on the generalized polynomial chaos expansion (gPC) method, our approach does not impose any physical priori and is mathematically rigorous and versatile. We have demonstrated the reliability and efficiency of the proposed method by estimating the bulk modulus of Ti3SiC2 and Sb2Te3 with comparison to conventional methods.
KW - ab initio calculations
KW - Bulk modulus
KW - Generalized polynomial chaos expansion
KW - Uncertainty quantification
UR - https://www.scopus.com/pages/publications/85064454454
U2 - 10.1016/j.commatsci.2019.04.022
DO - 10.1016/j.commatsci.2019.04.022
M3 - 文章
AN - SCOPUS:85064454454
SN - 0927-0256
VL - 165
SP - 7
EP - 12
JO - Computational Materials Science
JF - Computational Materials Science
ER -