Accelerated screening of sensitive and selective MoO3-based gas sensing materials by combining first-principles and machine learning approach

A large amount of hybrid metal oxides can be proposed for detecting various gases from rapidly developing of 2D materials and elemental doping technologies, and the key issue is how to search the large parameter space in an efficient way. First-principles approach can calculate molecular-scale electronic properties and provide a guide of material design for experiments, however, it is too time-consuming to explore all the possible metal oxides doped with different elements. Herein, we develop a novel framework via combining first-principles and machine learning, with the aim of enabling more efficient and practical screening of MoO3-based gas sensors than by experiments or first-principles alone. Owing to the high accuracy of first-principles calculations verified by experimental results, we demonstrate the reliability of the proposed methods for evaluating gas sensing performance. By proposing a set of new descriptors including d-band center and average bond length with demanding low-cost calculations, we significantly reduce the amount of required training data. In particular, the gradient boosting regression algorithm exhibits high R-square value of 0.96 and low mean absolute error value of 0.22, indicating superior reliability of the model. This work opens an avenue for quickly screening of novel metal oxides and other nano-materials with superior sensitivity and selectivity for gas detection.