posted on 2024-02-29, 14:03authored byXiaowen Li, Jian Qiu, Heping Cui, Xianping Chen, Jiabing Yu, Kai Zheng
Efficient and rapid screening of target materials in
a vast material
space remains a significant challenge in the field of materials science.
In this study, first-principles calculations and machine learning
algorithms are performed to search for high out-of-plane piezoelectric
stress coefficient materials in the MXene functional database among
the 1757 groups of noncentrosymmetric MXenes with nonzero band gaps,
which meet the criteria for piezoelectric properties. For the monatomic
MXene testing set, the random forest regression (RFR), gradient boosting
regression (GBR), support vector regression (SVR), and multilayer
perceptron regression (MLPR) exhibit R2 values of 0.80, 0.80, 0.89, and 0.87, respectively. Expanding our
analysis to the entire MXene data set, the best active learning cycle
finds more than 140 and 22 MXenes with out-of-plane piezoelectric
stress coefficients (e31) exceeding 3
× 10–10 and 5 × 10–10 C/m, respectively. Moreover, thermodynamic stabilities were confirmed
in 22 MXenes with giant piezoelectric stress coefficients and 9 MXenes
with both large in-plane (d11 > 15
pm/V)
and out-of-plane (d31 > 2 pm/V) piezoelectric
strain coefficients. These findings highlight the remarkable capabilities
of machine learning and its optimization algorithms in accelerating
the discovery of novel piezoelectric materials, and MXene materials
emerge as highly promising candidates for piezoelectric materials.