Kavli Affiliate: Jeffrey B. Neaton
| First 5 Authors: Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla P. Gomes
| Summary:
Machine learning for materials discovery has largely focused on predicting an
individual scalar rather than multiple related properties, where spectral
properties are an important example. Fundamental spectral properties include
the phonon density of states (phDOS) and the electronic density of states
(eDOS), which individually or collectively are the origins of a breadth of
materials observables and functions. Building upon the success of graph
attention networks for encoding crystalline materials, we introduce a
probabilistic embedding generator specifically tailored to the prediction of
spectral properties. Coupled with supervised contrastive learning, our
materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for
predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate
Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating
predictions with ab initio calculations and thereby discovering candidate
thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework
for predicting spectral properties of materials via strategically incorporated
machine learning techniques.
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