Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction.

In this work, inspired by Phillips's ionicity theory in solid-state physics, we directly sort out the critical factors of the band gap's feature correlations in the machine learning architected with the Lasso algorithm. Even based on a small 2D materials data set, we can fundamentally approach an accurate and rational model about the band gap and exciton binding energy with robust transferability to other databases. Our machine learning outputs can reveal the exact physics pictures behind the predicted quantity as well as the "secondary understanding" of the correlation between the approximated physics models in exciton. This work stresses the significant value of physics endorsement on the machine learning (ML) algorithm and provides a symbolic regression solution for the "few-shot" training scheme for ML technology in materials science. Moreover, physics-inspired secondary understanding could be an essential supplement for ML in scientific research fields.