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.

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机器学习：使用欧几里得神经网络直接预测状态的声子密度（Adv. Sci. 12/2021）

声子态密度是控制材料热特性的关键特性，但计算或测量并非易事。具有全晶体对称性的神经网络允许使用少量训练数据预测声子态密度，接近 ab initio 精度，但效率显着提高，如 Mingda Li、Zhantao Chen 的文章编号 2004214 所示， Nina Andrejevic、Tess Smidt 和同事。这项工作可以对具有优异热性能的材料进行直接结构性能设计。