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

Clicks: 236
ID: 33209
2019
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Ranked #25 of 38 articles by views in The journal of physical chemistry letters

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Abstract
Here in this work, inspired by Phillips' 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 dataset, 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 stressed the significant value of physics endorsement on the ML algorithm and provided a symbolic-regression solution for the "Few-Shot" training scheme for the ML technology in materials science. Moreover, physics-inspired secondary understanding could be an essential supplement for machine learning in scientific research fields.
Reference Key
liang2019phillipsinspiredthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liang, Jiechun;Zhu, Xi;
Journal The journal of physical chemistry letters
Year 2019
DOI
10.1021/acs.jpclett.9b02232
URL
Keywords Keywords not found

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