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.