ABC₂-type short-wave infrared photodetector materials discovered via high-throughput screening and machine learning

Rapid discovery of short-wave infrared (SWIR) detection materials requires efficient strategies to identify candidates with suitable bandgaps, favorable carrier transport properties, and structural stability. Here, we propose a high-throughput screening (HTS) framework that integrates machine learning (ML) models with density functional theory (DFT) calculations to accelerate the prediction and validation of infrared-detection materials (see Figure 1). Using a curated dataset of 1327 I–X–VI chalcogenide compounds retrieved from the Materials Project database, we trained five regression models-random forest, gradient boosting, support vector regression, extreme gradient boosting, and decision tree-to predict electronic bandgaps with high accuracy and computational efficiency. The optimized extreme gradient boosting regression (XGBR) model delivers a test-set coefficient of determination (R²) of 0.945, a mean absolute error (MAE) of 0.150 eV, and a mean squared error (MSE) of 0.056 eV, with a 5-fold cross-validation (R²) of 0.927, verifying its robust prediction performance and generalization ability. This ML-guided screening highlights five promising chalcogenides: KGaSe₂, KGaTe₂, KInSe₂, KInTe₂, and CsInTe₂. These candidates were further evaluated using first-principles DFT calculations to assess their band structures, density of states, and carrier effective masses. Among them, KGaSe₂ exhibits a direct bandgap of ~0.8 eV, low effective mass, and excellent thermodynamic stability, making it a highly attractive candidate for SWIR detection. This work demonstrates the power of combining ML and DFT in accelerating the discovery of IR optoelectronic materials and provides a scalable, generalizable approach for next-generation photodetector design.