Band Gap Prediction of Two-Dimensional Materials Using a Gradient-Boosted Feature Selection Approach | JoVE Visualize

Area of Science:

Materials Science
Computational Materials Science
Condensed Matter Physics

Background:

Two-dimensional (2D) inorganic crystals are crucial for advanced electronic and optoelectronic devices due to unique properties like high carrier mobility and tunable band gaps.
Data-driven approaches are increasingly used for predicting material properties, offering computationally efficient alternatives to traditional methods, especially for screening candidate materials.
Existing data-driven models often focus on 3D bulk materials, leaving a gap in predictive capabilities for 2D layered, van der Waals, and ultrathin film materials.

Purpose of the Study:

To develop and validate data-driven models for predicting key properties of 2D inorganic materials: thermodynamic stability, metallicity, and electronic band gap.
To ensure the developed models are fully interpretable, providing insights into feature relevance for property prediction.
To assess the models' performance on both in-distribution and out-of-distribution datasets for robust material screening.

Main Methods:

Utilized open-source databases (Alexandria_2D, C2DB, MC2D, 2DMatpedia) to compile a dataset of 2D materials.
Employed chemically relevant elemental, physical, and compositional features as input for the models.
Applied statistical and gradient-boosted feature selection to reduce dimensionality and enhance model interpretability using SHapley Additive exPlanations (SHAP).

Main Results:

Classifiers for thermodynamic stability and metallicity achieved high accuracy (89.7%) and F1-scores (0.832 and 0.870, respectively).
The band gap regressor demonstrated strong performance on the in-distribution test set with an R-squared of 0.883.
The band gap model showed moderate predictive ability on an out-of-distribution dataset, with an R-squared of 0.334, highlighting the challenges and potential for further improvement in predicting unseen material properties.

Conclusions:

Data-driven models, coupled with effective feature selection, provide accurate and interpretable predictions for 2D material properties.
These explainable surrogate models are effective for high-throughput screening, accelerating the discovery of novel 2D materials for electronic and optoelectronic applications.
The study validates the utility of machine learning in materials science for predicting thermodynamic stability, metallicity, and electronic band gaps in 2D materials.