Descriptor engineering in machine learning regression of electronic structure properties for 2D materials.

ABSTRACT: We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R[Formula: see text] greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R[Formula: see text] of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly im

SUBMITTER: Dau MT 

PROVIDER: S-EPMC10070413 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature