Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients.

ABSTRACT

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.