Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Abstract

Electrons and phonons give rise to important properties of materials. The machine learning framework Mat2Spec vastly accelerates their computational characterization, enabling discovery of materials for thermoelectrics and solar energy technologies.