Machine Learning: Direct Prediction of Phonon Density of States With Euclidean Neural Networks (Adv. Sci. 12/2021)

Phonon density‐of‐states is a key property that governs materials thermal properties but is nontrivial to compute or measure. A neural network that carries full crystal symmetry allows a prediction of phonon density‐of‐states using a small volume of training data, approaching ab initio accuracy but with significantly increased efficiency, as demonstrated in article number 2004214, by Mingda Li, Zhantao Chen, Nina Andrejevic, Tess Smidt, and co‐workers. This work enables direct structure‐property design of materials with superior thermal properties. [Image: see text]